Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of data-communication networks, and more particularly to a system and a method for zero-downtime, transaction-based updating and optimization of access-control rules in message queuing telemetry transport (MQTT) enabled message brokers deployed within Internet-of-Things (IoT) environments.

BACKGROUND
[0002] Message queuing telemetry transport (MQTT) brokers are widely used in low-bandwidth, publish-and-subscribe data-communication networks, particularly for Internet-of-Things (IoT) deployments in healthcare, industrial automation, energy distribution, and smart-building systems. To prevent unauthorized publication or subscription, each broker enforces an access-control list (ACL) that specifies which clients may read from, or write to, particular topic hierarchies.
[0003] In a typical production environment, the ACL must be updated frequently, whether to onboard a new device, revoke a compromised credential, or refine a customer’s privilege set. Commercially available brokers generally apply a revised ACL by restarting the broker or forcing all clients to reconnect. Even a sub-second interruption can disrupt ventilator telemetry in a hospital, halt a manufacturing line, or drop supervisory-control messages in a smart-grid substation. Administrators therefore face an undesirable trade-off between timely security updates and uninterrupted quality of service.
[0004] As the number of topics and wild-card patterns grows, the ACL file itself becomes complex. Existing broker plug-ins and text-based linters provide only superficial syntax checks; they cannot detect deep semantic conflicts, redundant wildcard coverage, or hidden privilege-escalation paths. Consequently, human operators spend significant effort combing through rule files, and inadvertent misconfigurations may leave exploitable security gaps or slow the broker’s authorization logic.
[0005] Current tool-chains likewise offer little predictive insight into whether a newly proposed rule set actually reflects real-world traffic patterns. Over-privileged clients often remain unnoticed, while unnecessary rules accumulate and inflate maintenance overhead. In critical infrastructures, such shortcomings translate into prolonged vulnerability windows and higher total cost of ownership for large-scale MQTT deployments.
[0006] To overcome at least the foregoing limitations, there exists a need for an access-control-management approach that applies updated rules without any client disconnection, validates rule correctness beyond mere syntax, and aligns security privileges with observed usage while minimizing operational complexity for administrators.

OBJECTS OF THE PRESENT DISCLOSURE
[0007] A general object of the present disclosure is to provide a system and method that enable zero-downtime updating of access-control rules in an MQTT message-broker environment.
[0008] Another object of the present disclosure is to maintain uninterrupted client sessions while replacing an active rule set through an atomic, transaction-based file-swap procedure.
[0009] Another object of the present disclosure is to validate each proposed rule update by performing syntax compliance checks, cryptographic-integrity verification, and graph-based detection of permission conflicts and redundancies before the rules are applied.
[0010] Another object of the present disclosure is to monitor client-session continuity and automatically roll back to a previously verified rule set when a validation failure or session anomaly is detected.
[0011] Another object of the present disclosure is to analyze historical client-access patterns with machine-learning techniques so as to predict potential security vulnerabilities and to recommend least-privilege rule adjustments.
[0012] Another object of the present disclosure is to generate natural-language explanations that describe the security implications of the recommended rule adjustments, thereby facilitating informed administrative decision-making.
[0013] Another object of the present disclosure is to log every rule-update transaction together with validation results, conflict-analysis findings, and optimization recommendations to persistent storage for audit and compliance purposes.
[0014] Another object of the present disclosure is to optimize validation throughput by distributing rule-validation tasks across a multithreaded, work-stealing queue architecture, enabling dynamic load balancing and linear scalability with processor cores.
[0015] Another object of the present disclosure is to construct a memory-efficient, compressed-sparse-row (CSR) encoded graph of access-control relationships and apply graph-theoretic techniques such as transitive reduction and topological sorting to eliminate redundancies and determine conflict-free rule-application sequences.
[0016] Another object of the present disclosure is to detect permission conflicts using Bloom filters during graph traversal, allowing for constant-time membership testing with a false-positive rate of less than 1%, thereby improving validation performance in large-scale deployments.

SUMMARY
[0017] Aspects of the present disclosure generally relate to the field of data-communication networks, and more particularly to a system and a method for zero-downtime, transaction-based updating and optimization of access-control rules in message queuing telemetry transport (MQTT) enabled message brokers deployed within Internet-of-Things (IoT) environments.
[0018] An aspect of the present disclosure pertains to a system for zero-downtime access-control management in a message broker. The system includes a network interface, a memory, and a processor coupled to the network interface and the memory. The memory stores instructions that, when executed, the processor is configured to receive an updated set of access-control rules using the network interface. The processor is also configured to prepare, using an atomic access-control-list (ACL) update layer, a transaction by assigning a transaction identifier to the updated set of access-control rules, and write the updated set of access-control rules into a temporary staging area for an isolated validation. The processor is also configured to validate the updated set of access-control rules. The processor is also configured to apply the validated set of access-control rules to the message broker through an atomic replacement operation without disconnecting active client connections. The processor is also configured to verify successful application of the validated set of access-control rules by performing a post-application cryptographic-integrity check and monitoring client-session continuity to detect anomalies. The processor is also configured to automatically initiate a rollback operation (202E) to restore a previously verified set of access-control rules upon detection of a validation failure or a session anomaly. The processor is also configured to analyze the validated set of access-control rules using a graph-based pattern-analysis module by constructing a directed acyclic graph representing topic hierarchies, and detecting, within the directed acyclic graph, at least one of: permission conflicts, and redundancies.
[0019] In one embodiment, the processor is configured to validate the updated set of access-control rules, prior to application, by performing syntax-compliance checking, conflict detection, and cryptographic-integrity verification.
[0020] In one embodiment, the processor is configured to validate the updated set of access-control rules includes generating a first cryptographic hash value of the staged rules within the temporary staging area, and compare the first cryptographic hash value with an expected reference value.
[0021] In one embodiment, the processor is configured to validate the updated set of access-control rules using a multithreaded validation engine that employs a work-stealing queue architecture to dynamically redistribute validation tasks among multiple threads based on processor load conditions.
[0022] In one embodiment, the processor is configured to monitors client-session continuity after applying the validated set of access-control rules to the message broker to detect anomalies.
[0023] In one embodiment, the processor is configured to automatically rollback to a previous set of access-control rules upon detection of one of: a validation failure, an access-denial anomaly, or a disruption in client-session continuity.
[0024] In one embodiment, the processor is configured to execute a multi-level rollback protocol including a first rollback level triggered by cryptographic-integrity verification failure, a second rollback level triggered by detection of statistical anomalies in client-session continuity, and a third rollback level triggered by an administrator-initiated override. Herein, each rollback level restores a previously verified set of access-control rules and maintains a cryptographic audit trail.
[0025] In one embodiment, the processor is configured to monitor client-session continuity by analyzing timing characteristics of MQTT heartbeat messages using an exponentially-weighted moving average and detecting anomalies based on a modified Z-score threshold.
[0026] In one embodiment, the processor is configured to analyze, using the predictive-intelligence layer, historical client-topic access patterns by extracting a multi-dimensional feature vector including at least one of: access frequency, session duration, topic hierarchy depth, wildcard pattern usage, and payload statistics, and applying a machine learning classifier to detect over-privileged access-control configurations.
[0027] In one embodiment, the processor is configured to calculate, using the predictive-intelligence layer, a risk score for each client-topic pair based on a privilege utilization ratio and a client-specific risk coefficient, and to generate ranked access-control rule modification recommendations prioritized by security impact.
[0028] In one embodiment, the processor is configured to analyze historical client-access patterns across topic hierarchies using a predictive-intelligence layer that applies machine-learning techniques to predict potential security vulnerabilities.
[0029] In one embodiment, the processor is configured to perform an atomic file replacement operation as part of the atomic replacement operation.
[0030] In one embodiment, the processor is configured to create a transactionally isolated copy of the access-control rules using a copy-on-write file cloning operation prior to performing the atomic file replacement operation.
[0031] In one embodiment, the processor is configured to generate an immutable audit chain including a sequence of audit records, each audit record including at least one of: a cryptographic hash of the current record, a hash of a previous audit record, a digital signature, a nanosecond-resolution timestamp, and one or more compliance tags associated with regulatory standards.
[0032] In one embodiment, the processor is configured to construct a directed acyclic graph representing access-control relationships using a compressed sparse row (CSR) encoding format, and to optimize the directed acyclic graph by performing at least one of: transitive reduction to eliminate redundant rule paths, and topological sorting to determine a conflict-free rule-application order.
[0033] In one embodiment, the processor is configured to detect permission conflicts during traversal of the directed acyclic graph by using Bloom filters to perform constant-time membership testing of permission sets.
[0034] In one embodiment, the predictive-intelligence layer is configured to generate optimized access-control-rule recommendations based on the historical client-access patterns and provides natural-language explanations of associated security implications.
[0035] In one embodiment, the system further includes an input/output interface communicatively coupled to the processor, wherein the input/output interface is configured to receive the updated set of access-control rules from an administrator device.
[0036] Another aspect of the present disclosure pertains to a method for zero-downtime access-control management in a message broker. The method includes receiving, using a network interface, an updated set of access-control rules for the message broker. The method also includes preparing, using an atomic access-control-list update layer, a transaction by assigning a transaction identifier to the updated set of access-control rules. The method also includes writing the updated set into a temporary staging area for an isolated validation. To validate the updated set of access-control rules, the method also includes performing at least one of: syntax-compliance checking, preliminary permission-conflict detection, and cryptographic-integrity verification. The method also includes applying the validated set of access-control rules to the message broker through an atomic replacement operation without disconnecting active client connections. The method also includes verifying successful application of the validated set of access-control rules by performing a post-application cryptographic-integrity check and monitoring client-session continuity to detect anomalies. The method also includes initiating rollback automatically to restore a previously verified ruleset upon detection of a validation failure or a session anomaly. The method also includes analyzing the validated set of access-control rules using a graph-based pattern-analysis module by constructing a directed acyclic graph representing topic hierarchies and detecting at least one of: permission conflicts, and redundancies.
[0037] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0038] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components, or circuitry commonly used to implement such components.
[0039] FIG. 1 illustrates an exemplary block diagram of a system (100) for zero-downtime access-control management for an MQTT message broker, in accordance with an embodiment of the present disclosure.
[0040] FIG. 2 illustrates a functional architecture (200) for zero-downtime access-control management, in accordance with an embodiment of the present disclosure.
[0041] FIG. 3 illustrates an exemplary block diagram of a computer system (300), in accordance with an embodiment of the present disclosure.
[0042] FIG. 4 illustrates an exemplary block diagram of a server system (400), in accordance with an embodiment of the present disclosure.
[0043] FIG. 5 illustrates an exemplary flow diagram of a method (500) for zero-downtime access-control management for an MQTT message broker, in accordance with an embodiment of the present disclosure.
[0044] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DETAILED DESCRIPTION
[0045] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0046] The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0047] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0048] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0049] In message queuing telemetry transport (MQTT)-based Internet-of-Things (IoT) deployments, updating broker access-control rules typically requires restarting the broker or forcing every client to reconnect. Even brief interruptions can halt telemetry in critical healthcare systems, stall industrial control loops, or create data-loss windows in smart-grid platforms. As rule files grow, administrators also struggle to detect overlapping wild-card patterns, redundant permissions, and latent privilege-escalation paths. Existing linters offer only superficial syntax checks and provide no insight into real-world usage, leaving over-privileged clients unnoticed and widening the attack surface.
[0050] The disclosed architecture introduces an atomic rule-update pipeline that stages new rules in an isolated sandbox, validates syntax, detects semantic conflicts via graph analysis, and verifies cryptographic integrity before a single atomic swap inserts the rules into the broker, all while preserving live client sessions. A predictive-intelligence layer concurrently mines historical access logs with machine-learning models to identify over-privileged clients, forecast security vulnerabilities, and generate least-privilege rule recommendations accompanied by human-readable justifications. All operations are orchestrated through software modules executed by a general-purpose processor and network interface, eliminating the need for specialized hardware.
[0051] The present disclosure enables administrators to apply security updates without inducing downtime, thereby maintaining uninterrupted data flow across thousands of connected devices. Conflict detection and redundancy pruning reduce rule-set complexity, improving broker authorization performance and minimizing misconfiguration risk. Machine-learning-driven optimization aligns permissions with actual usage, shrinking the attack surface and simplifying compliance auditing. Collectively, such effects yield higher system availability, stronger security posture, and lower operational overhead for large-scale MQTT infrastructures.
[0052] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosures as defined by the appended claims.
[0053] Embodiments explained herein relate to the field of data-communication networks, and more particularly to a system and a method for zero-downtime, transaction-based updating and optimization of access-control rules in message queuing telemetry transport (MQTT) enabled message brokers deployed within Internet-of-Things (IoT) environments.
[0054] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-5.
[0055] FIG. 1 illustrates an exemplary block diagram of a system (100) for zero-downtime access-control management in a message-queuing telemetry transport (MQTT) environment, in accordance with one or more embodiments of the present disclosure. The system (100) may include a message broker (102), one or more publisher clients (106), one or more subscriber clients (107), an administrator device (104), and a management host that houses a network interface (108), a processor (110), a memory (112) and an input/output (I/O) interface (114).
[0056] The message broker (102) may be implemented as any MQTT-compliant broker, whether open-source or proprietary. The broker (102) may support different versions of MQTT and may reside on-premises, at the network edge, or in a cloud instance. In operation, the broker (102) may receive PUBLISH packets from publisher clients (106) and deliver corresponding messages to subscriber clients (107) whose topic subscriptions match the published topics.
[0057] Each publisher client (106) may be, but is not limited to, an IoT sensor, an industrial controller, or a mobile application that transmits telemetry to the broker (102). Each subscriber client (107) may be a monitoring dashboard, an analytics engine, or another IoT device that receives data from the broker (102). Although FIG. 1 depicts three publisher clients (106) and three subscriber clients (107) for ease of illustration, any practical deployment may include hundreds or thousands of such clients.
[0058] The administrator may interact with the system (100) through the administrator device (104), which may be realised as a laptop computer, a tablet, or a server-side administrative console, but is not limited thereto. The administrator device (104) may transmit an updated set of access-control rules to the management host via a secure transport such as, but not limited to, Secure Shell (SSH), Hypertext Transfer Protocol Secure (HTTPS), or a dedicated management application-programming interface (API).
[0059] The management host may include the network interface (108), which may be, but is not limited to, a wired Ethernet adapter, a Wi-Fi module, or a virtual network interface presented by a cloud hypervisor. The network interface (108) may facilitate bidirectional data exchange with the message broker (102) and the administrator device (104). The processor (110) may be a multi-core CPU, a system-on-chip (SoC), or a virtual CPU allocated by an infrastructure-as-a-service (IaaS) provider, but is not limited thereto. The memory (112) may include, but is not limited to, volatile RAM for run-time data structures and non-volatile storage for executable instructions and rule files. The I/O interface (114) may expose a command-line shell, a RESTful endpoint, or a graphical dashboard, but is not limited thereto, that enables local or remote configuration.
[0060] During normal operation, publisher clients (106) may publish messages to the broker (102) on specific topics, while subscriber clients (107) may subscribe to one or more topics of interest. The broker (102) may consult its active access-control rules to authorise each PUBLISH or SUBSCRIBE request. When the administrator device (104) submits new rules, the management host may receive the rules via the network interface (108), process them in the processor (110), and store them in the memory (112). Updated set of the access-control rules may then be applied to the broker (102) without disconnecting the publisher clients (106) or subscriber clients (107).
[0061] The configuration shown in FIG. 1 is merely illustrative. Additional components, such as redundant network interfaces, hardware security modules, or edge gateways, may be incorporated without departing from the scope of the present disclosure.
[0062] FIG. 2 illustrates a functional architecture (200) for zero-downtime access-control management, in accordance with one or more embodiments of the present disclosure. The functional architecture (200) may include an atomic access-control list (ACL) update layer (202), a graph-based pattern analysis layer (204), and a predictive intelligence layer (206). Each layer may be responsible for distinct stages of rule management, validation, optimization, and risk analysis.
[0063] The atomic ACL update layer (202) may be configured to prepare and apply updates to the access-control rules without interrupting active client connections. The atomic ACL update layer (202) may further include several functional modules, such as a transaction preparation module (202A), a temporary staging module (202B), an atomic replacement module for rule application without disconnection (202C), a cryptographic integrity check module (202D), a rollback on failure module (202E), and a syntax compliance checking module (202F).
[0064] The graph-based pattern analysis layer (204) may be configured to analyse the updated access-control rules by building a directed acyclic graph (DAG) representation of the topic hierarchies and permissions. The graph-based pattern analysis layer (204) may include a DAG creation module (204A), a conflict detection module (204B), and a redundancy detection module (204C) for improving rule-set clarity and avoiding hidden permission escalations.
[0065] The predictive intelligence layer (206) may be configured to apply machine learning techniques for proactive security and operational insights. The predictive intelligence layer (206) may include an access-pattern analysis module (206A), a vulnerability prediction module (206B), an optimized rule recommendation module (206C), and a natural language explanation module (206D) for generating human-readable summaries of potential security risks.
[0066] The sequence shown in FIG. 2, from atomic rule updating, through graph-based conflict detection, to predictive optimization, illustrates an exemplary processing flow. However, other sequences, parallelizations, or modular reconfigurations may also be used without departing from the scope of the present disclosure.
[0067] It will be understood that the functional architecture (200), including the atomic ACL-update layer (202), the graph-based pattern-analysis layer (204), the predictive-intelligence layer (206), and their respective sub-modules, is presented for conceptual clarity and does not imply distinct hardware circuits. Each layer may be realised in whole or in part as software instructions stored in the memory (112) of the system (100) and executed by the processor (110). Accordingly, references to elements (202, 204, 206, 202A–202F, 204A–204C), and (206A–206D) should be interpreted as logical or programmatic constructs rather than discrete physical components, unless expressly stated otherwise.
[0068] In one embodiment, the processor (110) may be configured to receive, via the network interface (108), an updated set of access-control rules intended for enforcement by the message broker (102). Upon receipt, the processor (110) may be configured to prepare, using an atomic access-control-list (ACL) update layer (202), a transaction (202A) by assigning a transaction identifier to the updated set of access-control rules, and write the updated set of access-control rules, as part of the transaction (202A), into a temporary staging area (202B) for an isolated validation.
[0069] In one embodiment, the processor (110) may be configured to validate the updated set of access-control rules. the processor (110) may be configured to apply the validated set of access-control rules to the message broker (102) through an atomic replacement operation (202C) without disconnecting active client connections. The processor (110) may also be configured to verify successful application of the validated set of access-control rules by performing a post-application cryptographic-integrity check and monitoring client-session continuity to detect anomalies. The processor (110) may also be configured to automatically initiate a rollback operation (202E) to restore a previously verified set of access-control rules upon detection of a validation failure or a session anomaly. The processor (110) may also be configured to analyze the validated set of access-control rules using a graph-based pattern-analysis module (204) by constructing a directed acyclic graph (204A) representing topic hierarchies, and detecting, within the directed acyclic graph (204A), at least one of: permission conflicts (204B), and redundancies (204C).
[0070] In one exemplary embodiment, the processor (110) may be configured to analyse the now-active access-control rules by invoking a graph-based pattern-analysis module (204). The graph-based pattern-analysis module (204) may be configured to construct a directed acyclic graph (DAG) (204A) that represents the hierarchical relationships among MQTT topics and associated permissions. The processor (110) may then be configured to detect permission conflicts (204B), such as overlapping wild-card patterns or contradictory allow/deny entries, by traversing the DAG (204A) and evaluating node-to-node intersections.
[0071] In this manner, the system (100) may be configured to update, validate, and analyse access-control rules dynamically, enabling the message broker (102) to enforce revised security policies without incurring client disconnections or service interruptions.
[0072] In one embodiment, prior to applying the updated set of access-control rules, the processor (110) may be configured to validate the rules through a three-stage procedure. First, the processor (110) may be configured to invoke a syntax-compliance-checking module (202F) to parse each rule statement, confirm that wild-card placement, topic delimiters, and permission keywords, conform to a predefined grammar, and reject any rule that fails to satisfy such syntactic constraints. Next, the processor (110) may be configured to employ the conflict-detection capability (204B) of the graph-based pattern-analysis module (204) to identify semantic clashes such as mutually exclusive allow and deny entries or redundant wild-card coverage that could otherwise result in unintended privilege escalation or rule shadowing. Finally, the processor (110) may be configured to perform a cryptographic-integrity verification (202D) by computing a secure hash of the staged rule file and comparing the computed value with a reference hash supplied by the administrator device or generated during transaction preparation. The processor (110) may also be configured to proceed to the atomic replacement operation only if all three validation stages, syntax compliance, conflict detection, and integrity verification, complete successfully.
[0073] In one embodiment, validating the updated set of access-control rules may include generating a first cryptographic hash value of the rule file stored in the temporary staging area (202B). The processor (110) may be configured to read the entire staged ruleset, apply a secure one-way hash algorithm such as, but not limited to, SHA-256 or any function of comparable collision resistance, and obtain a first hash output. The processor (110) may then be configured to compare the first cryptographic hash value with an expected reference cryptographic hash value, the latter having been generated during transaction preparation or provided by the administrator device (104). Validation may be considered successful when the two cryptographic hash values (i.e., the first cryptographic hash value and the expected reference cryptographic hash value) match, thereby confirming that no bit-level alteration of the staged rules has occurred between preparation and application.
[0074] In one embodiment, the processor (110) may validate the updated set of access-control rules using a multithreaded validation engine that includes a thread pool configured to execute validation subtasks in parallel. The validation engine may include a dynamic task distribution mechanism that employs a work-stealing queue architecture. In the work-stealing queue architecture, each processing thread may maintain its own task queue, and idle threads may opportunistically retrieve ("steal") validation tasks from the queues of busier threads.
[0075] The validation tasks may include, but are not limited to, syntax-compliance checking of access-control rule expressions (202F), graph-based conflict detection (204B), and cryptographic-integrity verification (202D). These tasks may be decomposed into granular, independently executable units and assigned to individual threads within the thread pool.
[0076] The work-stealing queue architecture may implement lock-free or low-contention data structures to enable high-throughput parallelism while minimizing synchronization overhead. The system (100) may dynamically balance the workload across the available threads based on current processor load conditions, such as CPU utilization metrics or task-queue occupancy levels. In one implementation, the validation engine may further support priority queues for time-sensitive validations, enabling the system (100) to process mission-critical rule updates with preferential scheduling. The processor (110) may also include performance counters and feedback mechanisms to monitor thread-level validation throughput and adapt a thread-pool size or task scheduling policy accordingly.
[0077] The use of the work-stealing queue architecture may enable linear scalability of the validation layer up to high core-count processor environments, such as 16-core or 32-core server-class systems. As a result, the validation engine may support enterprise-grade performance benchmarks, including the ability to validate over, for example, 40,000 access-control rules per second without bottlenecks or lock contention.
[0078] In one embodiment, after the validated set of access-control rules has been applied to the message broker (102), the processor (110) may be configured to monitor client-session continuity to detect anomalies. The processor (110) may further be configured to poll or subscribe to broker-generated session-status events, track keep-alive heartbeats, or sample connection-state metrics exposed by the message broker (102). If any client session unexpectedly terminates, stalls, or reports an authentication failure within a configurable observation window, the processor (110) may also be configured to log the anomaly, raise an alert through the I/O interface (114), and optionally trigger further validation or rollback procedures.
[0079] In one exemplary embodiment, tracking keep-alive heartbeats may include configuring the processor (110) to maintain, for each connected client, a keep-alive timer initialised to the interval that the client declares during the MQTT CONNECT procedure. The processor (110) may be configured to decrement the keep-alive timer in real time and reset it whenever the client transmits any MQTT control packet or whenever the broker (102) returns a corresponding response.
[0080] When no application data or other control packet is sent during the negotiated interval, the MQTT specification requires the client to issue a control packet known as PINGREQ (Ping Request), an empty, heartbeat-style request whose sole purpose is to confirm that the network path between the client and the broker is still active. Upon receiving a PINGREQ, the broker must reply with PINGRESP (Ping Response) inside the same interval. The processor (110) may therefore be configured to treat the timely arrival of each PINGREQ/PINGRESP pair as a keep-alive heartbeat for the associated client connection.
[0081] If the keep-alive timer for a particular client reaches zero before a qualifying packet (e.g., PINGREQ, PUBLISH, SUBSCRIBE) is observed, the processor (110) may be configured to classify the event as a missed heartbeat, mark the session as anomalous, and depending on policy, generate an alert, trigger a diagnostic probe, or automatically roll back the most recently applied access-control rules to restore normal operation.
[0082] In one embodiment, the processor (110) may also be configured to initiate a rollback module (202E) that automatically restores a previously active set of access-control rules upon detection of one of: a validation failure, an access-denial anomaly, or a disruption in client-session continuity. During transaction preparation, the processor (110) may be configured to store a cryptographically signed snapshot of the current rules together with an associated transaction identifier. If a subsequent validation failure occurs such as a hash mismatch reported by the cryptographic-integrity check (202D) or an unrecoverable error from the syntax-compliance checker (202F), or if an access-denial anomaly is observed (for example, the message broker (102) unexpectedly rejecting PUBLISH or SUBSCRIBE requests that should be authorised), or if a disruption in client-session continuity is detected (for example, missed keep-alive heartbeats or a sudden surge in disconnect notifications), the processor (110) may be configured to trigger the rollback module (202E). The rollback module (202E) may then atomically replace the suspect ruleset with the stored snapshot, restore the broker’s authorisation state, log the reversal event, and optionally transmit an alert through the I/O interface (114) or the network interface (108) to inform an administrator device (104) that the newly applied rules have been revoked due to the detected anomaly.
[0083] In one embodiment, the processor (110) may be configured to execute a multi-level rollback protocol for restoring previously verified access-control rule configurations upon detection of rule deployment issues. The rollback protocol may include three graduated rollback levels, each tailored to a specific category of failure severity or recovery condition. At a first rollback level, the system (100) may automatically initiate an immediate recovery process upon detecting a failure in cryptographic-integrity verification. For example, if the hash value of the applied access-control rules does not match the expected reference hash, indicating potential tampering or corruption, the system may restore a prior version of the access-control rules known to be cryptographically intact.
[0084] At a second rollback level, the processor (110) may initiate a restoration procedure in response to statistical anomalies detected in client-session continuity. Such anomalies may be identified by the session-monitoring module based on deviations in MQTT heartbeat behavior or other protocol-compliant client interactions. If abnormal client disconnections or latency spikes are observed, the system may determine that the applied rules have adversely affected broker performance and may rollback to a stable prior state.
[0085] At a third rollback level, the system (100) may support administrator-initiated overrides through a management interface. An administrator may manually select a previously verified configuration from a rollback history and trigger its reapplication in response to operational or policy-driven concerns. Each rollback level may restore access-control rules that were previously validated and stored in a secure configuration history. To ensure traceability and regulatory compliance, the rollback protocol may maintain a cryptographic audit trail for each rollback event. Such audit trails may include cryptographic hashes (e.g., SHA-3), timestamps, administrator identifiers, and event-level metadata stored within an immutable audit chain as described in other embodiments.
[0086] In one embodiment, the processor (110) may be configured to monitor client-session continuity by analyzing timing characteristics of MQTT heartbeat signals exchanged between the message broker (102) and connected client devices. The monitoring may include observing the interval patterns of PINGREQ and PINGRESP messages transmitted during each client session, which are defined as part of the MQTT protocol for ensuring connection liveness. To detect subtle or progressive disruptions in session continuity, the processor (110) may apply an exponentially-weighted moving average (EWMA) technique to the sequence of measured heartbeat intervals. The EWMA may allow the system (100) to assign greater weight to recent observations while still retaining sensitivity to historical trends, enabling effective smoothing and short-term anomaly detection.
[0087] In parallel, the system (100) may evaluate deviations in heartbeat timing using a modified Z-score method, wherein each new interval is statistically compared to the EWMA-derived baseline. A modified Z-score threshold (e.g., absolute Z-score > 3.5) may be used to classify outlier intervals as potential anomalies. These anomalies may indicate emerging connectivity degradation, client-side instability, or unintended side-effects of newly applied access-control rules. Upon detection of such anomalies, the processor (110) may flag the session for further validation or recovery. In some embodiments, this may trigger downstream actions such as alert generation, temporary rule quarantining, or initiation of a rollback operation (202E), depending on the severity and recurrence of the anomaly pattern.
[0088] The combination of EWMA smoothing and Z-score thresholding may allow the system to proactively detect issues that would otherwise be missed by static timeout checks or binary session states, thereby enhancing the robustness and resilience of access-control enforcement in high-availability environments.
[0089] In one embodiment, analysing the updated set of access-control rules may include generating a directed acyclic graph (204A) that represents all topic hierarchies and the corresponding allow-or-deny permissions. Each vertex of the graph may model a concrete topic or a wildcard pattern, while each edge may denote a parent–child relationship, thereby preserving the hierarchical structure mandated by MQTT topic syntax. Once the graph (204A) is constructed, the processor (110) may be configured to perform a graph-based intersection analysis that traverses overlapping paths and computes set intersections between permissions assigned to the same or subordinate vertices. Whenever the traversal identifies an edge or vertex pair that carries mutually exclusive permissions such as simultaneous “allow” and “deny” directives for an identical topic pattern, the analysis may flag a permission conflict (204B). The processor (110) may be configured to record each detected conflict, rank conflicts by severity or breadth of coverage, and surface the results to the administrator through the I/O interface (114) or an external dashboard.
[0090] In one embodiment, the processor (110) may be configured to use a predictive-intelligence layer (206) to analyse historical client-access patterns spanning multiple topic hierarchies. The predictive-intelligence layer (206) may be configured to ingest broker log files, real-time telemetry streams, or any other audit records that indicate which clients have published to, or subscribed from, specific topics during a configurable observation window. Within the predictive-intelligence layer (206), a machine-learning module (206A) may extract temporal and statistical features such as access frequency, burstiness, and topic depth, and train or update predictive models that characterise normal versus anomalous usage. By applying the trained models, the processor (110) may be configured to predict potential security vulnerabilities (206B), including over-privileged clients, dormant wildcard patterns that could be exploited, or unexpected cross-hierarchy access attempts. The predictive-intelligence layer (206) may assign a risk score to each identified condition and store or surface such scores through the I/O interface (114) or the network interface (108) for administrative review.
[0091] In one embodiment, the predictive-intelligence layer (206) may further be configured to leverage the risk scores and feature vectors derived from historical client-access patterns to generate optimised access-control-rule recommendations (206C). The processor (110) may then be configured to compare each client’s observed topic usage with its currently granted permissions and may propose rule amendments that enforce a least-privilege posture, for example, narrowing overly broad wildcard patterns or revoking rarely used topic authorisations. For every recommended change, the predictive-intelligence layer (206) may also be configured to provide a natural-language explanation (206D) that concisely states the security implication i.e., potential security vulnerabilities (206B), such as “Client A has not published to topic X in the past 30 days; revoking the permission reduces unnecessary exposure without impacting normal operation.” Such recommendations and explanations may be surfaced through the I/O interface (114) or delivered to an administrator device (104) over the network interface (108), thereby enabling informed, human-readable decision-making before the optimised rules are accepted or rejected.
[0092] In one embodiment, the predictive-intelligence layer (206) may be configured to analyze historical access behavior of clients interacting with the message broker (102) across various topics. This analysis may focus on identifying over-privileged access-control configurations, i.e., cases where granted permissions exceed the actual usage patterns of the client, thereby representing potential security or performance risks.
[0093] To support this analysis, the processor (110) may be configured to extract a multi-dimensional feature vector for each client-topic pair, using accumulated session activity logs or real-time telemetry data collected by the system (100). The feature vector may include one or more behavioural and structural attributes relevant to access-control evaluation. The one or more behavioural and structural attributes may include the access frequency, which indicates how often a client accesses a given topic over a defined observation window; the session duration, reflecting the average or cumulative connection time for the client; the topic hierarchy depth, representing the structural depth of the topic path within the MQTT namespace (e.g., building/floor/zone/sensor); the wildcard pattern usage, which identifies whether the client employs wildcard subscriptions such as + or #; and payload statistics, including numerical descriptors such as mean, standard deviation, skewness, or kurtosis of the message payloads transmitted or received. The features may serve as inputs to a machine learning classifier or risk scoring model within the predictive-intelligence layer (206) to support over-privilege detection and access-control optimization.
[0094] The predictive-intelligence layer (206) may apply a machine learning classifier, such as, but not limited to, a decision tree, random forest, or gradient-boosted model, to these feature vectors in order to identify clients whose access patterns suggest unused or excessive privileges. The model may be trained using labelled historical data that distinguishes between minimally-privileged and over-privileged configurations, allowing it to generalize to new clients or rule sets.
[0095] Once a potential over-privilege condition is detected, the system (100) may flag the relevant rule or client-topic binding for administrative review or automated remediation. In some implementations, this analysis may also feed into rule optimization or risk scoring modules (as described in other embodiments), enabling dynamic policy refinement and least-privilege enforcement.
[0096] In one embodiment, the processor (110) may be configured to calculate, using the predictive-intelligence layer (206), a risk score for each client-topic pair based on a combination of access-behavior analytics and contextual sensitivity. The predictive-intelligence layer (206) may access historical or real-time usage data to assess the actual utilization of access-control privileges granted to each client.
[0097] The privilege utilization ratio may be computed as the proportion of actual accesses to granted accesses for a given topic or topic group. For instance, if a client has been granted publish or subscribe rights to five topics but has only accessed two of them within a meaningful time window, the utilization ratio may be calculated as 40%. The utilization ratio may be smoothed over time or segmented by topic hierarchy depth.
[0098] In addition, the system (100) may assign a client-specific risk coefficient that reflects the relative sensitivity or threat level associated with the client. The client-specific risk coefficient (or the client risk coefficient) may be based on attributes such as the client’s role, historical anomaly frequency, geographic location, authentication strength, or previous privilege violations.
[0099] The predictive-intelligence layer (206) may then compute a composite risk score for each client-topic pair by combining the privilege utilization ratio with the corresponding client risk coefficient. In one example implementation, the risk score may be calculated as:

[00100] Using these risk scores, the system (100) may generate a prioritized list of access-control rule modification recommendations. Each recommendation may indicate which privileges could be revoked or restricted with minimal operational impact while significantly improving overall security posture. The list may be sorted in descending order of security impact, enabling administrators to address the highest-risk over-privileged bindings first. In some embodiments, these recommendations may be presented through a dashboard interface along with explanatory metrics, and may optionally include a natural-language justification (as described in related embodiments).
[00101] In one embodiment, the processor (110) may be configured to perform an atomic file replacement operation as part of the atomic replacement operation (202C) executed during access-control rule updates in the message broker (102). The atomic file replacement operation may ensure that the updated set of access-control rules is swapped into effect without introducing partial or inconsistent states that could disrupt client connectivity or message routing.
[00102] The atomic replacement operation (202C) may include invoking a file-system level atomic operation, such as a “rename()” system call on POSIX-compliant platforms, which enables the system (100) to replace the existing access-control rules file with a newly validated version in a single, indivisible step. Because such operations are atomic at the kernel level, they may eliminate the risk of race conditions or rule misapplication during live updates.
[00103] During this process, the original set of access-control rules may remain in active use by the message broker (102) until the newly validated rules have been fully written to a temporary staging area (202B). Once the new rules are ready and verified, the processor (110) may perform the atomic replacement by swapping the file pointer from the old file to the new one in a single operation, preserving all active socket descriptors and MQTT session state in volatile memory.
[00104] The atomic file replacement may guarantee zero downtime, ensuring that no client disconnections or broker restarts are required. The broker may continue to serve publish/subscribe traffic using existing memory-resident session structures while the ACL rules are updated transparently in the background. In some implementations, the atomic replacement operation may be further optimized using kernel-level mechanisms such as copy-on-write cloning, secure permissions, or memory barriers to maintain cache coherence and integrity during rule transition.
[00105] In one embodiment, the processor (110) may be configured to create a transactionally isolated copy of the access-control rules prior to performing the atomic file replacement operation (202C). The transactionally isolated copy may be generated using a copy-on-write file cloning operation, which allows the system (100) to duplicate the contents of the existing ruleset without duplicating the underlying data blocks, thereby conserving system memory and reducing I/O overhead.
[00106] The copy-on-write file cloning operation may include invoking a file-system-level mechanism, such as, for example, but not limited to, the FICLONE or FICLONERANGE input/output control (or ioctl) commands supported by Linux-based file systems like Btrfs or XFS, that enables an efficient block-level clone to be created instantly. The cloned file may initially share physical data blocks with the original file, with separate copies created only upon write operations, ensuring both performance and isolation. Herein, FICLONE and FICLONERANGE are system calls related to file cloning and copy-on-write functionality, primarily used in Linux for efficient file operations. MQTT, on the other hand, is a lightweight messaging protocol designed for IoT devices and other resource-constrained environments. Further, Linux-based file systems like Btrfs or XFSBtrfs and XFS are both modern, high-performance file systems available for Linux. Btrfs is a copy-on-write filesystem that offers features like snapshots, subvolumes, and built-in Redundant Array of Independent Disks (RAID) support, making it suitable for advanced storage needs. XFS, on the other hand, is known for its high performance, scalability, and reliability, particularly for handling large files and concurrent I/O operations.
[00107] Once the cloned ruleset is created in the temporary staging area (202B), the processor (110) may apply validation steps, such as syntax checking, conflict detection, and cryptographic-integrity verification, on the isolated copy without affecting the live production ruleset used by the message broker (102). This isolation may ensure that the validation process is non-disruptive and that no unverified rules reach the broker's operational context. Upon successful validation, the processor (110) may proceed to perform the atomic file replacement operation (202C), using the validated and transactionally isolated clone to overwrite the active ruleset. This combination of copy-on-write cloning for isolation and atomic file replacement for application may enable the system (100) to perform ACL updates with both transactional safety and zero downtime.
[00108] In some embodiments, metadata associated with the cloned ruleset, such as timestamps, validation checksums, and administrator identifiers, may also be preserved or enhanced using extended file attributes or secure logging, thereby supporting later rollback or audit operations if necessary.
[00109] In one embodiment, the processor (110) may be configured to generate an immutable audit chain for recording system-critical events associated with access-control rule updates, validations, and rollbacks within the system (100). The immutable audit chain may include a sequence of cryptographically linked audit records, each securely bound to its predecessor to prevent unauthorized modification or deletion.
[00110] Each audit record may include a cryptographic hash of the current record’s contents, such as a SHA-3 digest, which uniquely identifies that record and detects tampering. It may also include a hash of the previous audit record, thereby establishing a cryptographic linkage between consecutive entries to form an unbroken chain. To ensure non-repudiation, each record may be digitally signed using an asymmetric key pair, such as Rivest-Shamir-Adleman secure hash algorithm (e.g., RSA-SHA256), associated with a trusted administrator or system module. Furthermore, the record may be timestamped with nanosecond-level precision using a high-resolution system clock, allowing for accurate reconstruction of system events. To support regulatory traceability, the record may also include one or more compliance tags that reflect applicable frameworks such as Sarbanes-Oxley Act (SOX), Health Insurance Portability and Accountability Act (HIPAA), or North American Electric Reliability Corporation Critical Infrastructure Protection (NERC-CIP), either auto-classified by the system or annotated by the administrator.
[00111] The processor (110) may append the new audit record to the chain, each time the access-control rules are updated, validated, rolled back, or overridden. In one implementation, the audit chain may be maintained in an append-only log format or as a secure ledger in non-volatile storage. To ensure long-term integrity, the system may periodically verify the audit chain by re-hashing its entries and confirming consistency with a stored root or anchor hash. Any deviation from the expected hash structure or signature verification may indicate tampering or unauthorized system activity, prompting alerts or triggering enforcement responses. Through this immutable audit mechanism, the system (100) may support verifiable rule management history, satisfy industry-specific compliance mandates, and reinforce operational accountability in distributed or regulated deployment environments.
[00112] In one embodiment, the processor (110) may be configured to construct a directed acyclic graph (DAG) to represent access-control relationships within the system (100). The DAG may model dependencies, hierarchies, and conflicts among topics and permissions defined in the access-control rule set applied to the message broker (102). Each node in the DAG may represent a topic, sub-topic, or permission set, and each directed edge may encode an access relationship, a topic inheritance structure, or a rule dependency.
[00113] To enable efficient in-memory representation and fast traversal, the DAG may be encoded using a compressed sparse row (CSR) format. In this encoding model, the graph may be stored as a compact set of three arrays that track non-zero entries, row boundaries, and column indices, thereby reducing memory usage compared to adjacency matrices, especially in sparse rule sets common to MQTT deployments. The CSR format may enable the processor (110) to traverse, update, or optimize the graph with minimal overhead even at large scale.
[00114] Once constructed, the DAG may be subjected to one or more graph-theoretic optimization procedures. In one example, the processor (110) may perform transitive reduction, which eliminates redundant edges between nodes where an indirect path already exists. Such process may help simplify rule interpretation by removing implied permissions that can be derived from intermediate nodes.
[00115] In another example, the processor (110) may perform topological sorting of the DAG nodes to determine a valid, conflict-free order of rule application. This may be particularly useful in scenarios involving rule overrides, policy layering, or dependency resolution across hierarchical topics. The topological order may ensure that precedence constraints and structural dependencies are honoured during access-control evaluation.
[00116] The graph-theoretic optimization procedures, when combined with CSR-based representation, may allow the system (100) to scale to tens of thousands of access-control rules while maintaining predictable processing time and reduced memory consumption. The resulting DAG structure may further support fast validation, redundancy detection, and consistency enforcement across complex rule environments.
[00117] In one embodiment, the processor (110) may be configured to detect permission conflicts during traversal of the directed acyclic graph (DAG). As the DAG represents access-control relationships across topics and client permissions, traversal of this graph may be necessary to identify overlapping, conflicting, or redundant rule sets that affect the integrity and enforceability of access policies within the system (100). To accelerate conflict detection, the system may use Bloom filters as an efficient probabilistic data structure for constant-time set membership testing. During DAG traversal, permission sets associated with nodes or edges may be encoded into Bloom filters. When the system (100) encounters a new or modified permission entry, it may test for potential conflict by querying the Bloom filter rather than performing a full linear scan or set comparison against previously visited nodes.
[00118] The use of Bloom filters may significantly reduce computational overhead, enabling constant-time (O(1)) membership testing regardless of the size of the permission set. In one implementation, the Bloom filters may be constructed using multiple independent hash functions and a bit array sized to maintain a false-positive rate of less than 1%. This ensures that conflict detection remains accurate and performant, particularly in large-scale deployments where the number of access-control rules may exceed tens of thousands.
[00119] Although Bloom filters may occasionally yield false positives, deterministic behavior and compact memory footprint of the Bloom filters make them particularly well-suited for high-speed preliminary conflict detection. In some embodiments, entries flagged by Bloom filters may undergo a secondary exact match check to confirm the presence of a true conflict before initiating remediation or reporting.
[00120] FIG. 3 illustrates an exemplary block diagram of a computer system (300), in accordance with one or more embodiments of the present disclosure. The computer system (300) may include a system bus (302) that communicatively couples multiple functional blocks, namely a processor (304), a main memory (306) (or volatile main memory (306)), a non-volatile read-only memory (308), a mass-storage device (310), a removable or external storage device (312), a network interface controller (NIC) (314), and one or more input/output (I/O) interfaces (316). A person skilled in the art will appreciate that the computer system (300) may include additional buses, additional processors, or additional communication ports without departing from the scope of the present disclosure.
[00121] The processor (304) may be realised as a multi-core central-processing unit, a system-on-chip, or a virtualised central-processing resource provisioned by a cloud-computing infrastructure. In certain embodiments, the processor (304) may incorporate dedicated sub-modules that execute access-control-management logic, transaction-validation routines, graph-analysis algorithms, and machine-learning models, although such sub-modules need not be illustrated separately.
[00122] The volatile main memory (306) may be implemented as dynamic random-access memory (DRAM) or static random-access memory (SRAM) that temporarily stores run-time data structures, intermediate transaction records, and executable code segments. The non-volatile read-only memory (308) may be realised as a programmable read-only memory (PROM), an electrically erasable PROM (EEPROM), or any other static storage technology suitable for retaining firmware instructions such as basic input/output system (BIOS) routines or secure boot loaders.
[00123] The mass-storage device (310) may provide persistent data storage and may be implemented using, but not limited to, magnetic hard-disk drives, solid-state drives, hybrid drives, or non-volatile memory express (NVMe) devices. The mass-storage device (310) may store access-control rule files, transaction logs, graph representations of topic hierarchies, and trained machine-learning weights. The external storage device (312) may be coupled to the system bus (302) via a high-speed interface such as Universal Serial Bus (USB), Thunderbolt®, or Peripheral Component Interconnect Express (PCIe) and may facilitate off-line backup or archival of rule-update snapshots.
[00124] The network interface controller (314) may be realised as a wired Ethernet adapter, a wireless transceiver, or a virtual network interface presented by a hypervisor. The NIC (314) may support full-duplex, bidirectional communication with external nodes, including a message broker, publisher or subscriber clients, and an administrator device, over any suitable packet-switched network such as a local-area network (LAN), a wide-area network (WAN), or the public Internet.
[00125] The I/O interfaces (316) may expose human-machine or programmatic interfaces such as universal asynchronous receiver-transmitter (UART) ports, keyboard-video-mouse (KVM) connectors, graphical user interfaces (GUI), command-line shells, or representational state-transfer (REST) endpoints. Such interfaces may enable an operator, an administrator device, or an external orchestration service to configure access-control parameters, retrieve audit logs, or initiate diagnostics.
[00126] The system bus (302) may conform to any suitable interconnect specification, such as Advanced High-performance Bus (AHB), PCIe, or a memory controller hub front-side bus, and may further bridge to subordinate buses carrying peripheral traffic. In an embodiment, the bus (302) may support direct-memory-access transactions to reduce processor overhead when transferring large rule-update payloads between the memory (306) and the mass-storage device (310).
[00127] Operator-facing components such as a display, a keyboard, or a pointing device may also be coupled, directly or through the I/O interfaces (316), to support local management. Remote operator and administrative access may additionally be provided through the network interface controller (314). The components described above exemplify but do not limit the possible configurations of the computer system (300); alternative or additional subsystems may be incorporated without departing from the scope of the present disclosure.
[00128] It will be appreciated that the functional blocks illustrated in FIG. 1, namely the processor (110), the memory (112), the network interface (108), the input/output interface (114) and any associated software layers, may be realised, in whole or in part, by the corresponding hardware and software components of the computer system (300) depicted in FIG. 2. For example, the processor (304) and the memory (306) of the computer system (300) may execute program instructions that implement the rule-transaction preparation, validation, graph-based analysis, and machine-learning functions otherwise attributed to the processor (110) and memory (112) of FIG. 1. Likewise, the network interface controller (314) of FIG. 2 may assume the communication duties of the network interface (108), while the I/O interfaces (316) may provide the operator or API access represented by the input/output interface (114). Accordingly, all methods, flows, or steps described herein with reference to FIG. 1 may be executed by, or within, the computer system (300) using its processor (304), memory (306), storage subsystems (310, 312), network interface controller (314), and I/O interfaces (316), without departing from the scope of the present disclosure.
[00129] FIG. 4 illustrates an exemplary block diagram of a server system (400), in accordance with one or more embodiments of the present disclosure. The server system (400) may be configured to receive updated rule sets, execute validation and optimisation logic, store historical access data, and expose management services to an administrator device or other orchestration layer.
[00130] In one embodiment, the server system (400) may include one or more processors (402). The processor(s) (402) may be realised as multi-core CPUs, system-on-chip (SoC) devices, field-programmable gate arrays (FPGAs), or any equivalent processing circuitry capable of executing computer-readable instructions. The processor(s) (402) may fetch and execute instructions stored in a memory (404) and thereby perform transaction preparation, graph-based conflict detection, machine-learning inference, and rollback orchestration.
[00131] The memory (404) may include a combination of volatile and non-volatile storage media, such as dynamic random-access memory (DRAM), static RAM, flash memory, or solid-state drives. The memory (404) may store program instructions, rule-snapshot files, graph data structures, machine-learning models, and audit logs. In certain implementations the memory (404) may be partitioned so that time-critical validation routines reside in high-speed tiers while archival rule histories reside in lower-cost tiers.
[00132] The server system (400) may further include one or more network interfaces (406) that facilitate bidirectional communication with a message broker, publisher or subscriber clients, and administrator devices. Each network interface (406) may support wired Ethernet, wireless LAN, or virtualised cloud networking and may implement Transport Layer Security (TLS) to protect in-transit rule updates and telemetry streams.
[00133] A processing engine (408), implemented as software logic executed by the processor(s) (402), may coordinate backend operations. The processing engine (408) may incorporate sub-modules for syntax-compliance checking, graph construction, permission-conflict detection, cryptographic-integrity verification, and automatic rollback. In some embodiments the processing engine (408) may expose a RESTful or gRPC API to allow external services to trigger validation cycles or query current risk scores. A RESTful API may expose hypertext-based endpoints that exchange resource representations over HTTP using standard verbs such as GET, POST, PUT, and DELETE, thereby enabling language-agnostic, stateless interactions between distributed components. A gRPC API may provide a high-performance, contract-based interface that serializes request and response messages in Protocol Buffers format and transports them over HTTP/2, thereby supporting low-latency, bi-directional streaming between client and server applications.
[00134] The server system (400) may also include a rule-and-telemetry database (410) operatively coupled to the processing engine (408). The database (410) may persist successive versions of access-control rules, transaction identifiers, client-access logs, and machine-learning feature vectors. The database (410) may support both real-time look-ups, such as fetching the active ruleset for a given broker, and batch analytics, such as generating weekly summaries of over-privileged clients.
[00135] In an embodiment, a machine-learning accelerator (412), which may be a graphics-processing unit (GPU), a tensor-processing unit (TPU), or an AI-optimized CPU extension, may be coupled to the processor(s) (402) via a high-bandwidth interconnect. The accelerator (412) may expedite feature extraction and inference tasks performed by the predictive-intelligence layer described with reference to FIG. 2.
[00136] One or more administrative interfaces (414) may also be provided. Each administrative interface (414) may present a command-line shell, a web dashboard, or an application-programming interface that enables authorized personnel or automated orchestration tools to submit rule updates, retrieve conflict reports, or download audit logs.
[00137] The architectural arrangement shown in FIG. 4 is illustrative only. Additional subsystems, such as redundant power supplies, hardware security modules for key management, load-balancing front ends, or distributed cache layers, may be integrated into the server system (400) without departing from the scope of the present disclosure.
[00138] It will be appreciated that the functional elements illustrated in FIG. 1, namely the processor (110), the memory (112), the network interface (108), the input/output interface (114) and any associated software layers, may be realized, in whole or in part, by the corresponding hardware and software components of the server system (400) shown in FIG. 4. For example, the processor(s) (402) and the memory (404) of the server system (400) may execute program instructions that implement the rule-transaction preparation, validation routines, graph-based conflict analysis, machine-learning inference, and rollback logic otherwise attributed to the processor (110) and memory (112) of FIG. 1. Likewise, the network interface(s) (406) may assume the communication duties of the network interface (108), while the administrative interfaces (414) may provide the operator or API access represented by the input/output interface (114). Accordingly, any method, flow, or step described herein with reference to FIG. 1 that calls upon elements 108, 110, 112, 114, or their sub-modules, may be executed by, or within, the server system (400) using its processor(s) (402), memory (404), storage subsystems (410), network interface(s) (406), and administrative interfaces (414), without departing from the scope of the present disclosure.
[00139] Referring now to FIG. 5, an exemplary flow diagram of a method (500) for zero-downtime access-control management is illustrated, in accordance with one or more embodiments of the present disclosure. The method (500) may be implemented by the processor (110) of the system (100).
[00140] At step (502), the method (500) may include receiving, using the network interface (108), an updated set of access-control rules for the message broker (102) from an administrator device or an external orchestration service. The updated access-control rules may be received in a structured format, such as a JavaScript Object Notation (JSON) document, Extensible Markup Language (XML) schema, or any machine-parsable configuration file suitable for programmatic interpretation. The received rules may define permissions associated with publishing or subscribing to various MQTT topic hierarchies and are intended for staged validation, conflict analysis, and atomic application to the message broker (102) without service disruption.
[00141] At step (504), the method (500) may include preparing, using an atomic access-control-list update layer (202), a transaction (202A) by assigning a transaction identifier to the updated set of access-control rules. Generation of the transaction (may also include a transaction identifier) may be performed by a transaction-preparation sub-module (for example, 202A) that may concatenate contextual metadata such as a current timestamp, an administrator device identifier, and a cryptographically secure random nonce and may hash the concatenated string with a collision-resistant algorithm (e.g., SHA-256) to yield a unique reference value. Concurrently, the temporary staging area (202B) may be provisioned as an isolated file-system directory, an in-memory buffer, or a containerized volume that is logically separated from the message broker’s active rule repository. All subsequent syntax checks, integrity verifications, and conflict-analysis operations may reference the transaction identifier for traceability, while the isolation afforded by the staging area (202B) may ensure that the updated set of access-control rules cannot influence live authorization decisions until full validation has been completed.
[00142] At step (506), the method (500) may include writing the updated set of access-control rules into the temporary staging area (202B) and computing a first cryptographic hash of the staged file. The processor (110) may be configured to serialize the received rule set, for example, as UTF-8 text or a structured JSON documentand may store the serialized content in the staging area (202B) created during step (504). Immediately after the write operation completes, the processor (110) may be configured to invoke a hashing routine that may apply a collision-resistant algorithm, such as SHA-256 or its functional equivalent, to produce a first hash value that uniquely represents the byte-level state of the staged file. The first (cryptographic) hash may be retained in volatile main memory (306) or persisted to the rule-and-telemetry database (410) so that later validation steps may verify the integrity of the staged rules before they are atomically applied to the message broker (102).
[00143] At step (508), the method (500) may include performing syntax-compliance checking (202F) on the staged rules to ensure grammatical correctness. A syntax-checking module within the atomic ACL-update layer (202) may parse each rule statement stored in the temporary staging area (202B) against a formal grammar that may define allowable permission keywords, MQTT topic delimiters, wildcard placements, and comment syntax. The processor (110) may be configured to iterate through the staged rules, tokenize each line, and verify that every token sequence conforms to the predefined grammar. Rules that contain malformed wildcards, missing permission verbs, or illegal characters may be flagged as errors, and the transaction identifier associated with the staging area (202B) may be marked as invalid. If no syntax errors are detected, the syntax-checking module (202F) may emit a pass status that permits the validation workflow to advance to subsequent conflict-detection and integrity-verification stages.
[00144] At step (510), the method (500) may include performing preliminary conflict detection (204B) on the staged rules by referencing a previously stored directed acyclic graph (DAG) (204A) that represents existing topic hierarchies and their associated permissions. A conflict-detection sub-module within the graph-based pattern-analysis layer (204) may ingest the freshly staged rules from the temporary staging area (202B) and temporarily overlay them onto the stored DAG (204A). The processor (110) may then be configured to traverse intersecting paths in the combined graph to identify semantic collisions such as overlapping wild-card patterns that carry contradictory allow-and-deny directives, or redundant rules that shadow narrower permissions, but not limited thereto. For each detected inconsistency, the conflict-detection module (204B) may log a descriptive entry linked to the active transaction identifier, enabling the system (100) to halt the validation workflow or request human review before the rules proceed to the integrity-verification step.
[00145] At step (512), the method (500) may include verifying the cryptographic integrity of the staged rules by comparing the first cryptographic hash value with an expected reference cryptographic hash value (202D). A cryptographic-verification sub-module within the atomic ACL-update layer (202) may retrieve the first hash value generated in step (506) and may obtain an expected reference cryptographic hash value that may have been (i) supplied by the administrator device (104) as part of a digitally signed manifest, or (ii) generated locally during transaction preparation and stored in volatile main memory (306) or the rule-and-telemetry database (410). The processor (110) may then be configured to perform a constant-time equality check between the two cryptographic hash values (i.e., the first cryptographic hash value and the expected reference cryptographic hash value). If the two cryptographic hash values match, the staged rules may be deemed unaltered and the validation workflow may proceed; if the values differ, the cryptographic-verification module (202D) may flag an integrity failure, record the event against the active transaction identifier, and inhibit further processing until manual review or an automatic rollback is invoked.
[00146] At step (514), the method (500) may include applying the validated set of access-control rules to the message broker (102) through an atomic replacement operation (202C) without disconnecting active client connections. To accomplish this, the processor (110) may be configured to invoke an atomic-update sub-module in the ACL-update layer (202) that may (i) create a shadow copy of the broker’s live ruleset in kernel or file-system space, (ii) swap the shadow copy with the validated rules from the temporary staging area (202B) using an operating-system primitive such as a rename-at or copy-on-write flag, and (iii) signal the broker (102) with a lightweight reload command. Because the swap occurs as a single, indivisible file-system operation, the broker (102) may reload the new rules in situ while maintaining established TCP sessions and MQTT state machines.
[00147] At step (516), the method (500) may include verifying successful application of the validated set of access-control rules by performing a post-application cryptographic-integrity check and monitoring client-session continuity to detect anomalies. The processor (110) may be configured to initiate a session-monitoring routine that tracks active client connections, verifies timely exchanges of keep-alive heartbeats (e.g., PINGREQ/PINGRESP messages), and observes resource metrics such as CPU usage, memory consumption, and broker thread health. During and immediately after the atomic replacement operation (202C), the processor (110) may also be configured to continuously evaluate whether any client sessions terminate unexpectedly, experience authentication failures, or exhibit abnormal resource patterns that could indicate misapplied access-control rules. Any detected anomaly may be correlated with the transaction identifier associated with the rule update and flagged for further evaluation, triggering alerts or automated corrective actions if necessary.
[00148] At step (518), the method (500) may include performing a cryptographic integrity verification of the access-control rules after application and, if either a hash mismatch or a session anomaly (as detected in step (516)) is observed, automatically initiating a rollback to a prior ruleset (202E). Specifically, the processor (110) may be configured to calculate a new cryptographic hash value of the access-control rules that the message broker (102) has loaded into the memory (112) and may compare the new cryptographic hash value against the first cryptographic hash generated in step (506). A discrepancy between these two values may indicate unintended alteration during the atomic replacement operation (202C). Independently, the processor (110) may be configured to consult the session-monitoring log to determine whether abnormal disconnects, missed keep-alive heartbeats, or resource spikes occurred during or immediately after the rule swap. Upon detecting either condition, the processor (110) may be configured to invoke the rollback module (202E), which may atomically restore the most recent, verified ruleset, update the broker’s authorization tables, and record the rollback event, together with the associated transaction identifier, in persistent storage for audit purposes.
[00149] At step (520), the method (500) may include constructing, with a graph-based pattern-analysis module (204), an updated directed acyclic graph (204A) that represents the current topic hierarchies and their associated permissions. The processor (110) may be configured to parse the newly activated access-control rules and map each explicit topic or wildcard pattern to a vertex within the graph. Parent–child edges may be created to preserve hierarchical relationships among topics (for example, “building/+/temperature” as a parent of “building/zone1/temperature”). Each vertex may be annotated with its effective allow-or-deny permission, enabling the graph (204A) to serve as a canonical data structure for subsequent conflict-detection and optimization steps.
[00150] At step (522), the method (500) may include detecting permission conflicts (204B) and redundancies (204C) within the updated directed acyclic graph (204A). The graph-based pattern-analysis module (204) may traverse parent–child paths to identify vertices that inherit mutually exclusive permissions for example, a parent topic that grants publish rights while a child topic explicitly denies the same rights. Such collisions may be flagged as permission conflicts (204B). In parallel, the module (204) may compare wildcard-based vertices with their concrete descendants to locate rule entries whose coverage is entirely subsumed by broader patterns, thereby flagging those entries as redundancies (204C). Each detected conflict or redundancy may be logged with its graph coordinates and severity level, providing actionable feedback for subsequent optimization or administrative review.
[00151] At step (524), the method (500) may include analyzing historical client-access patterns with a predictive-intelligence layer (206) by applying machine-learning techniques (206A) to predict potential security vulnerabilities (206B). To that end, the processor (110) may be configured to retrieve time-stamped publish-and-subscribe logs, authentication records, and broker metrics from the rule-and-telemetry database (410). The predictive-intelligence layer (206) may extract features such as access frequency per topic, average message size, burstiness, and temporal cycles, and may feed those features into a trained classification or anomaly-detection model residing in the machine-learning accelerator (412) or executed directly on the processor (402). The model (206A) may output risk scores that highlight over-privileged clients, seldom-used wildcard patterns, and anomalous cross-hierarchy access attempts. Each finding may be recorded as a predicted security vulnerability (206B), tagged with its probability or severity level, and forwarded to downstream optimization and reporting components for further action.
[00152] At step (526), the method (500) may include generating optimized access-control-rule recommendations (206C) based on the analyzed access patterns. Leveraging the risk scores and feature vectors produced in step (524), the predictive-intelligence layer (206) may compare each client’s observed topic usage with the permissions currently granted in the active rule set. Where excessive or unused privileges are detected, the processor (110) may be configured to formulate candidate rule revisions, for example, narrowing a broad wildcard pattern to concrete topic paths, revoking rarely invoked publish rights, or consolidating overlapping allow directives into a single entry. Each candidate change may be packaged with metadata that identifies the affected client, the impacted topics, and an estimated reduction in attack surface, enabling an administrator device (104) or an automated orchestration service to review or accept the recommendation.
[00153] At step (528), the method (500) may include producing natural-language explanations (206D) that describe the security implications of the optimized access-control-rule recommendations generated in step (526). The predictive-intelligence layer (206) may invoke a template-driven or language-generation routine that ingests each recommended rule change, together with its calculated risk score, affected client identifier, and impacted topic hierarchy, and may output a concise, human-readable summary such as: “Revoking wildcard permission building/+/temperature from Client A reduces unnecessary publish rights and mitigates potential data tampering on all building zones.” The natural-language explanations (206D) may accompany the corresponding recommendations when they are displayed through the administrative interface (414) or transmitted to an administrator device (104), thereby enabling non-technical stakeholders to understand the rationale and security benefits of each proposed rule adjustment.
[00154] At step (530), the method (500) may include logging the transaction identifier, validation results, conflict-analysis findings, and any generated recommendations to persistent storage, such as the rule-and-telemetry database (410), for audit purposes. The processor (110) may be configured to assemble an audit record that contains (i) the unique transaction identifier created in step (504), (ii) pass / fail outcomes for each validation stage (syntax compliance, conflict detection, and cryptographic-integrity verification), (iii) a list of detected permission conflicts (204B) and redundancies (204C) with their severity rankings, and (iv) the optimized rule recommendations (206C) together with their natural-language explanations (206D). The audit record may be time-stamped, digitally signed, and written to the database (410), thereby enabling later forensic analysis, regulatory compliance checks, or rollback correlation across multiple rule-update cycles.
[00155] It should be appreciated that the steps of the method (500) described herein are not limited to the specific sequence or structure outlined above. The method (500) may be implemented in various other ways, and the steps may be reordered, combined, omitted, or modified without departing from the scope or spirit of the present disclosure. The examples provided are for illustrative purposes only and are not intended to limit the invention to the specific embodiments described. Those skilled in the art will recognize that various modifications and adaptations may be made to the method (500) based on implementation-specific requirements.
[00156] It will be appreciated that one or more additional components may be incorporated, modified, or omitted in the implementation of the present disclosure without departing from the scope as defined by the appended claims. The described embodiments are merely illustrative, and variations in design, structure, or material selection may be made to suit specific applications. Any such modifications, equivalents, or substitutions are intended to be within the scope and spirit of the present invention as defined by the claims.
[00157] While the foregoing describes various embodiments of the present disclosure, other and further embodiments of the present disclosure may be devised without departing from the basic scope thereof. The scope of the present disclosure is determined by the claims that follow. The present disclosure is not limited to the described embodiments, versions, or examples, which are included to enable a person having ordinary skill in the art to make and use the present disclosure when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[00158] The present disclosure provides a system and method that update broker access-control rules without forcing client reconnections, thereby preserving continuous data flow in time-critical IoT deployments.
[00159] The present disclosure enables atomic, file-system–level replacement of rule sets, which eliminates transient states and markedly reduces the risk of partial or corrupted policy application.
[00160] The present disclosure delivers multi-phase validation, syntax checking, cryptographic integrity, and graph-based conflict analysis, that substantially lowers the likelihood of misconfiguration and unintended privilege escalation.
[00161] The present disclosure automates rollback to a previously verified rule set upon detection of validation failure or session anomaly, providing a self-healing safety net that minimizes administrative intervention.
[00162] The present disclosure employs machine-learning analysis of historical access patterns to highlight over-privileged clients and recommend least-privilege rule adjustments, thereby shrinking the attack surface and tightening security posture.
[00163] The present disclosure couples each recommended rule change with a natural-language explanation, making security implications readily understandable to non-specialist stakeholders and improving decision turnaround.
[00164] The present disclosure maintains a comprehensive, time-stamped audit trail including transaction identifiers, validation outcomes, conflict findings, and optimization suggestions that streamlines compliance reporting and forensic investigations.
[00165] The present disclosure improves validation scalability by distributing rule-checking tasks across a multithreaded, work-stealing architecture, thereby achieving linear throughput growth with available processor cores and ensuring responsiveness in enterprise-scale deployments.
[00166] The present disclosure optimizes rule-set processing through compressed sparse row (CSR) graph encoding and graph-theoretic techniques such as transitive reduction and topological sorting, enabling efficient detection of redundant and conflicting permissions with significantly reduced memory usage.
[00167] The present disclosure accelerates permission-conflict detection during graph traversal using Bloom filters, offering constant-time membership testing with a false-positive rate under 1%, which enables high-speed validation in environments with large rule volumes.
[00168] The present disclosure provides cryptographically linked, immutable audit chains that ensure tamper-evident recordkeeping for all rule updates and rollbacks, thus strengthening trustworthiness, regulatory compliance, and traceability across system lifecycle events.
, Claims:1. A system (100) for zero-downtime access control management in a message broker (102), the system (100) comprising:
a network interface (108);
a memory (112); and
a processor (110) communicatively coupled to the network interface (108) and the memory (112), wherein the memory (112) comprises instructions which, when executed by the processor (110), cause the processor (110) to:
receive, using the network interface (108), an updated set of access-control rules for the message broker (102);
prepare, using an atomic access-control-list (ACL) update layer (202), a transaction (202A) by assigning a transaction identifier to the updated set of access-control rules, and write the updated set of access-control rules into a temporary staging area (202B) for an isolated validation;
validate the updated set of access-control rules;
apply the validated set of access-control rules to the message broker (102) through an atomic replacement operation (202C) without disconnecting active client connections;
verify successful application of the validated set of access-control rules by performing a post-application cryptographic-integrity check and monitoring client-session continuity to detect anomalies;
automatically initiate a rollback operation (202E) to restore a previously verified set of access-control rules upon detection of a validation failure or a session anomaly; and
analyze the validated set of access-control rules using a graph-based pattern-analysis module (204) by constructing a directed acyclic graph (204A) representing topic hierarchies, and detecting, within the directed acyclic graph (204A), at least one of: permission conflicts (204B), and redundancies (204C).
2. The system (100) as claimed in claim 1, wherein the processor (110) is configured to validate the updated set of access-control rules, prior to application, by performing at least one of: syntax-compliance checking (202F), conflict detection (204B), and cryptographic-integrity verification (202D).
3. The system (100) as claimed in claim 2, wherein the processor (110) is configured to validate the updated set of access-control rules by generating a first cryptographic hash value of staged rules within the temporary staging area (202B) and comparing the first cryptographic hash value with an expected reference cryptographic hash value.
4. The system (100) as claimed in claim 2, wherein the processor (110) is configured to validate the updated set of access-control rules using a multithreaded validation engine that employs a work-stealing queue architecture to dynamically redistribute validation tasks among multiple threads based on processor load conditions.
5. The system (100) as claimed in claim 1, wherein the processor (110) is configured to monitor client-session continuity after applying the validated set of access-control rules to the message broker (102) to detect anomalies.
6. The system (100) as claimed in claim 5, wherein the processor (110) is configured to automatically rollback (202E) to a previous set of access-control rules upon detection of one of: a validation failure, an access-denial anomaly, or a disruption in client-session continuity.
7. The system (100) as claimed in claim 6, wherein the processor (110) is configured to execute a multi-level rollback protocol comprising:
a first rollback level triggered by cryptographic-integrity verification failure;
a second rollback level triggered by detection of statistical anomalies in client-session continuity; and
a third rollback level triggered by an administrator-initiated override,
wherein each rollback level restores a previously verified set of access-control rules and maintains a cryptographic audit trail.
8. The system (100) as claimed in claim 5, wherein the processor (110) is configured to monitor client-session continuity by analyzing timing characteristics of MQTT heartbeat messages using an exponentially-weighted moving average and detecting anomalies based on a modified Z-score threshold.
9. The system (100) as claimed in claim 1, wherein the processor (110) is further configured to analyze historical client-access patterns across topic hierarchies using a predictive-intelligence layer (206) that applies machine-learning techniques (206A) to predict potential security vulnerabilities (206B).
10. The system (100) as claimed in claim 9, wherein the predictive-intelligence layer (206) is configured to:
generate optimized access-control-rule recommendations (206C) based on the historical client-access patterns; and
provide natural-language explanations (206D) of associated security implications.
11. The system (100) as claimed in claim 9, wherein the processor (110) is configured to analyze, using the predictive-intelligence layer (206), historical client-topic access patterns by extracting a multi-dimensional feature vector comprising at least one of: access frequency, session duration, topic hierarchy depth, wildcard pattern usage, and payload statistics, and applying a machine learning classifier to detect over-privileged access-control configurations.
12. The system (100) as claimed in claim 9, wherein the processor (110) is configured to calculate, using the predictive-intelligence layer (206), a risk score for each client-topic pair based on a privilege utilization ratio and a client-specific risk coefficient, and to generate ranked access-control rule modification recommendations prioritized by security impact.
13. The system (100) as claimed in claim 1, further comprising an input/output (I/O) interface (114) communicatively coupled to the processor (110), wherein the input/output interface (114) is configured to receive the updated set of access-control rules from an administrator device (104).
14. The system (100) as claimed in claim 1, wherein the processor (110) is configured to perform an atomic file replacement operation as part of the atomic replacement operation (202C).
15. The system (100) as claimed in claim 15, wherein the processor (110) is configured to create a transactionally isolated copy of the access-control rules using a copy-on-write file cloning operation prior to performing the atomic file replacement operation.
16. The system (100) as claimed in claim 1, wherein the processor (110) is configured to generate an immutable audit chain comprising a sequence of audit records, each audit record comprising at least one of: a cryptographic hash of the current record, a hash of a previous audit record, a digital signature; a nanosecond-resolution timestamp, and one or more compliance tags associated with regulatory standards.
17. The system (100) as claimed in claim 1, wherein the processor (110) is configured to construct a directed acyclic graph representing access-control relationships using a compressed sparse row (CSR) encoding format, and to optimize the directed acyclic graph by performing at least one of:
transitive reduction to eliminate redundant rule paths; and
topological sorting to determine a conflict-free rule-application order.
18. The system (100) as claimed in claim 17, wherein the processor (110) is configured to detect permission conflicts during traversal of the directed acyclic graph by using Bloom filters to perform constant-time membership testing of permission sets.
19. A method (500) for zero-downtime access-control management in a message broker (102), the method (500) comprising:
receiving (502), using a network interface (108), an updated set of access-control rules for the message broker (102);
preparing (504), using an atomic access-control-list update layer (202), a transaction (202A) by assigning a transaction identifier to the updated set of access-control rules and writing (506) the updated set into a temporary staging area (202B) for an isolated validation;
validating the updated set of access-control rules, by at least one of: performing (508) syntax-compliance checking (202F), performing (510) preliminary permission-conflict detection (204B), and performing (512) cryptographic-integrity verification (202D);
applying (514) the validated set of access-control rules to the message broker (102) through an atomic replacement operation (202C) without disconnecting active client connections;
verifying (516) successful application of the validated set of access-control rules by performing a post-application cryptographic-integrity check and monitoring client-session continuity to detect anomalies;
automatically initiating (518) rollback operation (202E) to restore a previously verified set of access-control rules upon detection of a validation failure or a session anomaly; and
analyzing (520) the validated set of access-control rules using a graph-based pattern-analysis module (204) by constructing a directed acyclic graph (204A) representing topic hierarchies, and detecting at least one of: permission conflicts (204B), and redundancies (204C) within the directed acyclic graph (204A).