DESC:SYSTEM TO TRACK AND VERIFY MESSAGE INTEGRITY IN AN AVIONICS COMMUNICATION NETWORK
CROSS REFERENCE TO RELATED APPLICTIONS
The present application claims priority from Indian Provisional Patent Application No. 202421024553 filed on 27/03/2025, the entirety of which is incorporated herein by a reference.
TECHNICAL FIELD
The present disclosure generally relates to avionics communication networks. Further, the present disclosure particularly relates to tracking and verifying message integrity in an avionics communication network.
BACKGROUND
Avionics communication networks facilitate data exchange among various aircraft subsystems. Such networks employ multiple communication protocols and data transmission techniques to enable seamless operation of avionics components, flight control systems, and ground stations. Ensuring message integrity in avionics communication networks remains critical due to potential risks associated with message loss, data corruption, unauthorized modifications, and delays caused by interference, network anomalies, or hardware failures. Various techniques have been employed to address such issues; however, conventional methods are associated with several drawbacks that impact reliability, scalability, and efficiency in real-time aviation environments.
One technique utilizes cyclic redundancy check (CRC) methods to detect errors in transmitted messages. Such a technique appends a checksum to each message before transmission, and the receiving entity recalculates the checksum to verify data integrity. However, CRC methods are limited in detecting deliberate message alterations or sophisticated cyber threats. Moreover, such techniques fail to provide a mechanism for tracking message paths across multiple communication entities in a network.
Another widely used technique involves centralized logging systems to record transmitted and received messages. Such systems store communication data in a central repository for post-flight analysis and integrity verification. However, centralized logging systems are associated with risks such as single-point failures, data synchronization issues, and delays in retrieving message logs during real-time operations. Additionally, discrepancies in logged data may go undetected due to inconsistencies in synchronization between logging nodes, leading to difficulties in pinpointing message loss or unauthorized modifications.
Cryptographic hashing techniques have been employed to enhance data authenticity. Such techniques generate cryptographic hashes for messages, enabling integrity verification upon reception. However, conventional cryptographic hashing techniques do not provide an effective mechanism for tracking message paths across multiple communication entities, limiting their ability to detect real-time discrepancies. Moreover, cryptographic methods alone do not address fault localization, making it challenging to identify the source of message loss or unauthorized modifications in a multi-node communication environment.
Blockchain-based approaches have also been utilized to enhance data integrity in communication networks. Such approaches employ decentralized ledger storage to maintain immutable records of transmitted messages. However, conventional blockchain-based methods introduce high computational overhead, increased latency in message verification, and difficulties in integrating blockchain within avionics communication networks that operate under stringent real-time constraints. Additionally, existing blockchain implementations lack efficient verification mechanisms to analyze discrepancies in message logs across multiple communication entities, making real-time tracking and verification inefficient.
Various other techniques have also been used, such as message retransmission protocols and redundancy-based methods. Such techniques rely on retransmitting lost messages or duplicating transmissions across different channels. However, retransmission-based techniques introduce latency and excessive bandwidth consumption, while redundancy-based methods increase system complexity and hardware resource utilization. Additionally, such techniques do not address message tracking and verification requirements in real-time avionics environments.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and techniques for tracking and verifying message integrity in avionics communication networks.
SUMMARY
The aim of the present disclosure is to provide a system to track and verify message integrity in an avionics communication network, wherein the system enhances message integrity tracking, detects discrepancies, localizes faults, enables secure logging, and optimizes avionics communication reliability.
The present disclosure relates to a system to track and verify message integrity in an avionics communication network. A plurality of communication entities transmit and receive messages within the network. A message logging system is operatively associated with each communication entity. The message logging system logs each transmitted and received message, wherein each log comprises a cryptographic hash representing a message path from a source communication entity to a destination communication entity. A blockchain-based tracking mechanism interconnects the communication entities, wherein each transmitted message is a part of a blockchain. The blockchain comprises a plurality of blocks, each block linked by the cryptographic hash and comprising at least source information, message information, and a unique timestamp. A verification unit collects and compares the message logs from the communication entities. The verification unit performs a comparison between the messages sent by the source communication entity and the messages received by the destination communication entity. The verification unit further analyzes the logs from the intermediate communication entities to detect discrepancies and determine the location of message loss. A fault localization unit identifies a communication entity at which the message loss occurred based on the discrepancies detected in the message logs.
The system further comprises a hysteresis counter system that is operatively linked to the verification unit. The hysteresis counter system increments by a predefined value upon detection of the message loss to track the frequency of errors occurring in the communication network. The hysteresis counter system decrements by a predefined value upon detection of a correctly relayed message to maintain an adaptive balance in message integrity tracking. The hysteresis counter system generates an alert signal when a counter threshold is exceeded, thereby identifying persistent message loss occurrences and enabling corrective actions in avionics communication systems.
The system further comprises a sequential verification mechanism that is operable over multiple time frames to optimize message integrity verification. The sequential verification mechanism performs message integrity verification sequentially across subgroups of the communication entities. Such a sequential approach reduces verification workload and enables targeted analysis of message integrity at different intervals. The sequential verification mechanism distributes verification across distinct time frames to enhance computational efficiency and minimize latency in processing message integrity checks in avionics communication networks.
The verification unit is operatively linked to an adaptive loss-tolerance mechanism to dynamically adjust message loss thresholds. The adaptive loss-tolerance mechanism assesses real-time network conditions and modifies message loss thresholds accordingly. Such an adaptive adjustment prevents false alarms in fluctuating network conditions while maintaining the ability to detect and respond to significant message discrepancies. The adaptive loss-tolerance mechanism enhances resilience against varying communication link quality in avionics networks, enabling reliable and continuous monitoring of message integrity.
The message logging system supports a distributed ledger storage that synchronizes message logs across multiple redundant storage units. The distributed ledger storage prevents data inconsistencies by maintaining a synchronized and immutable record of message transactions across different communication entities. Such a decentralized approach minimizes risks associated with single-point failures and enhances data availability in avionics communication networks. The distributed ledger storage facilitates efficient retrieval and verification of logged messages, enabling seamless validation of message integrity across the network.
The system further comprises a message integrity scoring unit that assigns a confidence score to each message based on various integrity parameters. The message integrity scoring unit evaluates message paths, cryptographic hash consistency, and network reliability factors to determine a confidence level for each transmitted message. Such scoring enables the identification of high-risk message transmissions and provides a quantitative measure of message reliability. The message integrity scoring unit enhances decision-making processes in avionics communication by categorizing messages based on integrity confidence levels.
The system further comprises an anomaly response unit that is operatively associated with the verification unit. The anomaly response unit modifies the communication parameters upon detection of recurring patterns of message loss. Such a dynamic response enables real-time adaptation to persistent message discrepancies and prevents further data corruption. The anomaly response unit actively adjusts network protocols, transmission parameters, or redundancy measures to maintain optimal message integrity levels and minimize disruption in avionics communication networks.
The system further comprises an implementation unit that performs online monitoring during flight operations. The online monitoring process enables real-time message integrity tracking using a central communication entity. The system further performs offline post-flight analysis to examine collected message logs. Such post-flight analysis is utilized for predictive maintenance and dormant failure detection in avionics communication networks. The implementation unit enhances the ability to monitor and analyze message integrity both in real-time and during post-flight evaluations.
The fault localization unit is operatively linked to a flight phase mapping unit that correlates message losses with specific aircraft operational phases. The flight phase mapping unit identifies whether message integrity issues occur during specific phases such as take off, cruising, or landing. Such correlation enables targeted troubleshooting and proactive maintenance strategies. The flight phase mapping unit enhances diagnostic accuracy by providing phase-specific insights into communication integrity issues in avionics systems.
The message logging system enables cryptographic watermarking of the messages to enhance data authenticity. The cryptographic watermarking process embeds an additional layer of verification into the transmitted messages, preventing unauthorized modifications. Such a security mechanism strengthens message integrity verification by allowing authentication of transmitted messages at any stage within the communication network. The cryptographic watermarking approach provides an additional safeguard against data tampering and enhances trust in avionics message transmission.
In another aspect, the present disclosure provides a method for tracking and verifying message integrity in an avionics communication network. Messages are transmitted and received among a plurality of communication entities. Each transmitted and received message is logged using a message logging system operatively associated with each communication entity. Each log comprises a cryptographic hash representing a message path from a source communication entity to a destination communication entity. A blockchain-based tracking mechanism interconnects the communication entities, wherein each transmitted message forms a part of a blockchain. The blockchain comprises a plurality of blocks, each block linked by a cryptographic hash and comprising at least source information, message information, and a unique timestamp. A verification unit collects and compares the message logs from the communication entities. The verification unit performs a comparison between the messages sent by the source communication entity and the messages received by the destination communication entity. The verification unit further analyzes the logs from the intermediate communication entities to detect discrepancies and determine the location of message loss. A fault localization unit identifies a communication entity at which the message loss occurred based on the discrepancies detected in the message logs.
The method further comprises determining the time-series variations in message transmission across the communication entities using a message propagation analysis unit operatively associated with the verification unit. The message propagation analysis unit evaluates transmission delays, frequency fluctuations, and irregularities over time. Such an analysis provides deeper insights into message integrity patterns and helps identify potential anomalies that may affect communication reliability in avionics networks.
The method further comprises applying symmetric cryptographic functions to maintain data confidentiality using a lightweight encryption unit incorporated within the blockchain-based tracking mechanism. The lightweight encryption unit secures message transmissions while maintaining computational efficiency. Such encryption methods prevent unauthorized access to transmitted messages and make sure that message integrity verification remains secure within avionics communication networks.
The method further comprises initiating an alternative routing approach upon detection of persistent message loss using a self-healing communication mechanism operatively linked to the verification unit. The self-healing communication mechanism dynamically reroutes messages through alternative paths to bypass network failures. Such an approach improves resilience against persistent message discrepancies and enables continuous communication in avionics systems despite localized failures.
The method further comprises identifying emerging failure trends based on historical message loss data using an avionics health prediction unit operatively associated with the verification unit. The avionics health prediction unit utilizes past communication discrepancies to anticipate potential failures before they occur. Such predictive capabilities enable proactive maintenance measures and reduce the likelihood of unexpected communication failures in avionics networks.
The method further comprises categorizing message discrepancies based on severity and recurrence using an artificial intelligence-based classification engine operatively associated with the verification unit. The classification engine evaluates patterns in message integrity failures and assigns priority levels based on impact assessments. Such categorization allows efficient handling of communication issues and prioritization of failures in avionics systems.
The method further comprises logging critical message integrity events across multiple storage nodes using a decentralized event logging unit. The decentralized event logging approach reduces single-point failure risks by distributing event records across multiple locations.
The method further comprises estimating missing message content based on previously transmitted messages using a real-time data reconstruction unit operatively linked to the fault localization unit. The data reconstruction unit analyzes historical message patterns to infer missing data and restore partial message integrity in cases of transmission loss.
The method further comprises updating cryptographic keys periodically to enhance security against data breaches using a secure key rotation unit incorporated within the blockchain-based tracking mechanism. The secure key rotation unit prevents unauthorized access to encrypted messages by regularly refreshing encryption keys.
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BRIEF DESCRIPTION OF DRAWINGS
The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.
Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:
FIG. 1 illustrates a system 100 to track and verify message integrity in an avionics communication network, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a method 200 for tracking and verifying message integrity in an avionics communication network, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates a data flow diagram of message integrity tracking and verification in an avionics communication network, in accordance with the embodiments of the present disclosure.
FIG. 4 illustrates a message integrity verification workflow in an avionics communication network, in accordance with the embodiments of the present disclosure.
In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.

DETAILED DESCRIPTION
The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.
The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of a system to track and verify message integrity in an avionics communication network and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings, and which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.
As used herein, the term "system" refers to an arrangement of interconnected components that collectively perform a specific operation within an avionics communication network. Such system comprises multiple hardware and software elements configured for data transmission, logging, verification, and fault localization. The system may incorporate computing devices, storage units, communication interfaces, and cryptographic mechanisms. The system may operate in centralized or decentralized configurations, depending on operational requirements. Communication entities within the system may interact through wired or wireless communication channels, utilizing standardized protocols to facilitate message exchange. The system architecture may be modular or integrated, enabling interoperability with various avionics subsystems.
As used herein, the term "communication entity" refers to a network component that transmits and receives messages within an avionics communication network. Such communication entity facilitates data exchange between multiple avionics subsystems, including flight control systems, navigation units, and monitoring instruments. Communication technologies such as radio frequency communication, optical communication, and wired data buses may be employed depending on operational requirements. The communication entity may function as a source entity initiating a message transmission, a destination entity receiving the transmitted message, or an intermediate entity relaying messages within the network. Such communication entity may operate independently or in coordination with multiple network nodes to maintain seamless data transmission.
As used herein, the term "message" refers to a unit of data exchanged between communication entities in an avionics communication network. Such message may contain operational data, control signals, or status updates essential for avionics system functionality. Messages may be structured in fixed or variable-length formats, depending on the communication standard utilized. Messages may be unicast, multicast, or broadcast, depending on the intended recipients. Error detection techniques such as cyclic redundancy checks, parity bits, or cryptographic hashes may be comprised in the message structure to maintain data integrity during transmission and reception.
As used herein, the term "message logging system" refers to a component responsible for recording transmitted and received messages within an avionics communication network. Such message logging system captures, stores, and organizes message data for subsequent analysis and verification. Logged messages may be indexed based on parameters such as timestamp, source, and destination to facilitate retrieval and analysis. Storage methods may comprise non-volatile memory, cloud-based databases, and blockchain architectures. Message logging may operate in real-time or batch mode, depending on system requirements. Logged data may be subjected to encryption and compression techniques to enhance security and optimize storage efficiency. The message logging system supports tracking, auditing, and post-event analysis, enabling the detection of communication anomalies, security breaches, and data loss events within avionics networks.
As used herein, the term "blockchain-based tracking mechanism" refers to a system that utilizes blockchain technology to record and track messages exchanged within an avionics communication network. Such tracking mechanism comprises a series of interconnected data blocks, each storing cryptographic hashes and transaction records that verify message integrity. Each block contains information related to source identity, message content, timestamps, and validation parameters. Consensus mechanisms such as proof-of-work, proof-of-stake, or Byzantine fault tolerance may be utilized to authenticate and append new data blocks. The tracking mechanism prevents unauthorized modifications by enabling immutability and transparency in recorded messages. The blockchain-based tracking mechanism operates as a decentralized or distributed system to enhance data verification, security, and reliability in avionics communication environments.
As used herein, the term "verification unit" refers to a component responsible for analyzing message logs and detecting discrepancies in message integrity. Such verification unit performs comparisons between transmitted and received messages to identify potential errors, unauthorized modifications, or loss of data. The verification unit may utilize hash-based integrity checks, error detection algorithms, and statistical analysis techniques to assess message reliability. The verification process may be conducted in real-time or as part of a post-event auditing mechanism. The verification unit may be implemented as standalone hardware, software modules, or integrated avionics subsystems. The verification unit supports data integrity verification, message authentication, and fault analysis, enabling improved decision-making and security in avionics communication networks.
As used herein, the term "fault localization unit" refers to a component responsible for identifying the communication entity where message loss or discrepancies occur. Such fault localization unit analyzes message logs, performs correlation checks, and applies diagnostic techniques to determine the source of anomalies in message transmission. The fault localization unit may operate in real-time to identify message loss incidents or during post-event diagnostics for forensic analysis. Fault localization enables targeted corrective actions, allowing maintenance personnel to isolate and resolve network issues effectively. The fault localization unit may be implemented as an integrated avionics subsystem or as an external diagnostic tool interfacing with the communication network.
As used herein, the term "distributed ledger storage" refers to a data storage mechanism that synchronizes message logs across multiple storage nodes. Such distributed ledger storage prevents single-point failures by maintaining redundant copies of message logs in multiple locations. The storage system may utilize cryptographic hashing, data encryption, and redundancy techniques to secure and authenticate stored data.
As used herein, the term "cryptographic watermarking" refers to a security technique that embeds authentication markers within transmitted messages. Such cryptographic watermarking prevents unauthorized modifications and enhances data authenticity. The watermarking process may involve embedding encrypted validation markers within message content, allowing verification upon reception. Cryptographic watermarking supports secure message transmission in avionics networks.
As used herein, the term "comparison" refers to the process of evaluating transmitted and received messages to detect discrepancies in message integrity. Such comparison may involve checking message content, cryptographic hashes, timestamps, or metadata to determine whether any alterations, losses, or delays have occurred. The comparison process may be performed in real-time to detect transmission anomalies or may be conducted post-event for forensic analysis. Automated scripts, machine learning models, or cryptographic verification techniques may be used to perform comparisons at high speed and accuracy. The comparison may be applied to detect missing messages, unauthorized modifications, or inconsistencies in message logs. Results of the comparison process may be stored, logged, or used to trigger corrective actions in avionics communication systems.
As used herein, the term "discrepancy" refers to any inconsistency, alteration, or unexpected deviation in a transmitted or received message within an avionics communication network. Such discrepancy may occur due to message loss, unauthorized modification, corruption, or transmission delay. Discrepancies may be detected using hash comparisons, integrity checks, and anomaly detection techniques. Discrepancies may be caused by network congestion, signal interference, hardware malfunctions, or cybersecurity threats. Automated diagnostic tools, cryptographic verification systems, or artificial intelligence-based detection models may be used to analyze discrepancies in real-time or during post-event analysis. Identified discrepancies may trigger alerts, corrective actions, or fault localization procedures to maintain communication integrity in avionics networks.
As used herein, the term "loss" refers to the failure of a transmitted message to reach the intended destination within an avionics communication network. Such loss may result from signal degradation, hardware failures, software errors, or intentional interference. Message loss may be identified through sequence number tracking, acknowledgment-based transmission protocols, or hash-based integrity verification. Loss mitigation techniques may comprise retransmission protocols, redundant message transmission, or adaptive routing methods. The detection of message loss enables corrective actions such as retransmission requests, re-synchronization of communication entities, or network diagnostics to restore message integrity.
As used herein, the term "identification" refers to the process of determining the source, location, or nature of a discrepancy, fault, or anomaly within an avionics communication network. Such identification may be performed using message logs, integrity verification methods, or automated diagnostic tools. Identification methods may comprise hash verification, timestamp analysis, network topology mapping, or machine learning-based pattern recognition. The identification process may operate in real-time or as part of post-event forensic analysis. Identification of discrepancies or message losses enables targeted troubleshooting, root cause analysis, and system optimization to maintain network integrity.
As used herein, the term "threshold" refers to a predefined limit or boundary used to evaluate message integrity, detect anomalies, or trigger corrective actions in an avionics communication network. Such threshold may be applied to message loss rates, transmission delays, error counts, or cryptographic hash mismatches. The threshold may be dynamically adjusted based on real-time network conditions or may be set as a fixed parameter based on system specifications. If the threshold is exceeded, automated alerts, fault localization, or remedial measures may be initiated to maintain communication integrity.
As used herein, the term "correlation" refers to the process of analyzing relationships between message data, timestamps, and verification logs to identify patterns or inconsistencies. Such correlation may be applied to detect message loss trends, assess communication reliability, or localize faults within an avionics communication network. Correlation techniques may comprise statistical analysis, machine learning models, and cryptographic hash comparisons. Correlation data may be utilized for predictive maintenance, forensic analysis, or real-time anomaly detection.
As used herein, the term "retrieval" refers to the process of accessing stored message logs, verification records, or blockchain entries for integrity analysis and discrepancy detection. Such retrieval may be performed based on query parameters such as timestamps, message identifiers, or hash values. Examples of retrieval methods comprise indexed database queries, distributed ledger lookups, and forensic log searches. The retrieval process may be automated or manual, depending on system requirements. Retrieved data may be used for forensic investigations, anomaly detection, or real-time verification of message integrity.
As used herein, the term "synchronization" refers to the process of aligning message logs, timestamps, and verification data across multiple communication entities in an avionics communication network. Such synchronization makes sure that message records remain consistent and up to date across distributed storage systems or blockchain networks. Synchronization prevents data discrepancies, assures accurate integrity verification, and maintains consistency in fault localization processes.
As used herein, the term "tracking" refers to the continuous monitoring of message flow, integrity verification results, and anomaly detection patterns in an avionics communication network. Such tracking involves logging transmitted and received messages, verifying consistency across network nodes, and detecting deviations that may indicate message loss or unauthorized modifications. Tracking may be performed using blockchain-based records, distributed logging mechanisms, or real-time data analysis platforms. Tracking data may be stored, analyzed, or used to trigger corrective actions in case of detected discrepancies.
As used herein, the term "transmission" refers to the process of sending messages from a source communication entity to a destination communication entity in an avionics communication network. Transmission parameters may comprise message encoding, packet sequencing, error correction, and security protocols. Transmission reliability may be monitored using acknowledgment-based protocols, redundancy mechanisms, and automated retransmission strategies.
FIG. 1 illustrates a system 100 to track and verify message integrity in an avionics communication network, in accordance with the embodiments of the present disclosure. The system 100 comprises a plurality of communication entities 102 transmit and receive messages within an avionics communication network. Each communication entity 102 facilitates data exchange between avionics subsystems, flight control systems, navigation units, and monitoring devices. Communication may occur over wired or wireless channels using standardized communication techniques, including ARINC 429, MIL-STD-1553, or AFDX Ethernet. Each communication entity 102 may function as a source entity transmitting messages, a destination entity receiving messages, or an intermediate entity relaying messages through the network. Communication entities 102 may comprise transceivers, network routers, avionics communication interfaces, or other data transmission devices capable of exchanging information. Each communication entity 102 may support unicast, multicast, or broadcast transmissions depending on network topology and operational requirements. Error detection techniques, including cyclic redundancy checks, parity bits, and cryptographic verification, may be utilized to maintain message integrity during transmission and reception. Communication entities 102 may be synchronized using time-based coordination mechanisms such as GPS time references or network time synchronization techniques to enable proper sequencing of transmitted and received messages. Communication entities 102 may be arranged in a hierarchical or distributed architecture, allowing messages to be relayed across different network segments. Message prioritization mechanisms may be implemented to distinguish between high-priority control messages and low-priority data transmissions. The communication entities 102 may operate continuously or intermittently based on system requirements.
In an embodiment, the system 100 comprises a message logging system 104 that is operatively associated with each communication entity 102 and records each transmitted and received message. Message logs generated by the message logging system 104 comprise information such as message content, transmission timestamps, message source, and destination details. Each log entry comprises a cryptographic hash representing a message path from a source communication entity 102 to a destination communication entity 102. Message logging system 104 may store logs in temporary buffers, non-volatile memory, or distributed storage mechanisms depending on operational needs. The message logging system 104 may support real-time or batch-mode logging, making sure that messages are recorded systematically for subsequent verification. Logged data may be indexed based on timestamps, message identifiers, or communication entity 102 details, facilitating efficient retrieval and analysis. Message logs may be transmitted to a centralized or decentralized repository, assuring redundancy and accessibility for integrity verification purposes. The message logging system 104 may implement security measures such as encryption and access control mechanisms to prevent unauthorized modifications of stored logs. Logs generated by the message logging system 104 may be periodically analyzed to detect anomalies, message loss, or inconsistencies in communication patterns. Logged data may allow compatibility with various analysis tools. The message logging system 104 may support automated alerts or notifications when message integrity issues are detected.
In an embodiment, the system 100 comprise a blockchain-based tracking mechanism 106 that interconnects the communication entities 102, wherein each transmitted message is a part of a blockchain. The blockchain-based tracking mechanism 106 comprises a plurality of blocks, each block linked by a cryptographic hash and containing at least source information, message information, and a unique timestamp. Each message transmitted by a communication entity 102 is recorded as a blockchain transaction, making sure that message logs remain immutable and verifiable. The blockchain-based tracking mechanism 106 may operate as a permissioned or private blockchain network depending on system architecture. Each block in the blockchain-based tracking mechanism 106 is generated based on cryptographic validation techniques, making sure that modifications to previously recorded messages are not possible without detection. Message transactions within the blockchain-based tracking mechanism 106 may be validated through consensus-based verification techniques, preventing unauthorized alterations or tampering. Each message entry stored in the blockchain-based tracking mechanism 106 comprises unique identifiers corresponding to message source, transmission path, and validation records. Redundancy mechanisms may be implemented within the blockchain-based tracking mechanism 106 to prevent data loss and make sure continuous access to historical message records. The blockchain-based tracking mechanism 106 may support cryptographic signature verification, allowing authentication of recorded messages. Message integrity verification within the blockchain-based tracking mechanism 106 may be conducted in real-time or during scheduled verification cycles. Stored records within the blockchain-based tracking mechanism 106 may be distributed across multiple storage nodes, enabling resilience against network failures or unauthorized access attempts.
In an embodiment, the system 100 comprises a verification unit 108 that collects and compares the message logs from the communication entities 102 to detect message integrity issues. The verification unit 108 performs a comparison between messages sent by the source communication entity 102 and messages received by the destination communication entity 102. The verification unit 108 further analyzes logs from intermediate communication entities 102 to detect discrepancies and determine the location of message loss. The verification unit 108 may perform bitwise comparisons, hash verifications, or sequence-based analysis to determine whether messages have been modified, lost, or delayed. The verification unit 108 may operate as a centralized or distributed component within the avionics communication network, making sure that message verification processes align with system requirements. The verification unit 108 may analyze historical message logs stored within the blockchain-based tracking mechanism 106, allowing for retrospective integrity assessments. Anomalies detected by the verification unit 108 may trigger automated alerts or initiate corrective actions to restore communication reliability. The verification unit 108 may utilize artificial intelligence-based pattern recognition techniques to identify recurring message discrepancies and assess network stability over time. The verification unit 108 may also maintain audit trails of integrity verification processes, enabling traceability of message validation operations. The verification unit 108 may support configurable verification intervals, allowing message logs to be analyzed continuously, periodically, or upon detecting transmission anomalies. The verification unit 108 may interface with external diagnostic systems, facilitating integrated network analysis and performance monitoring.
In an embodiment, the system 100 comprises a fault localization unit 110 that identifies a communication entity 102 at which message loss occurred based on discrepancies detected in the message logs. The fault localization unit 110 analyzes logged data from the message logging system 104 and the blockchain-based tracking mechanism 106 to determine the point of failure within the communication network. The fault localization unit 110 may use timestamp analysis, sequence tracking, and cryptographic validation techniques to identify missing or altered messages. The fault localization unit 110 may correlate data from multiple communication entities 102 to pinpoint the precise location of message loss. The fault localization unit 110 may be implemented using statistical analysis methods, machine learning-based anomaly detection models, or pattern recognition techniques. The fault localization unit 110 may support automated reporting mechanisms, generating diagnostic summaries upon identifying message discrepancies. The fault localization unit 110 may interface with avionics maintenance systems, facilitating targeted troubleshooting of network faults. The fault localization unit 110 may track historical message loss patterns to assess communication network reliability over time. The fault localization unit 110 may support configurable loss detection thresholds, allowing for customized analysis based on network stability requirements. The fault localization unit 110 may be integrated with security monitoring systems, detecting potential unauthorized tampering attempts or cyber threats affecting message integrity. Logged data analyzed by the fault localization unit 110 may be stored for further reference, allowing aviation personnel to review past message discrepancies and implement corrective measures. The fault localization unit 110 may operate in conjunction with external redundancy mechanisms, assuring that identified message losses are addressed through retransmission protocols or alternate routing strategies.
In an embodiment, a hysteresis counter system may be operatively linked to the verification unit 108. The hysteresis counter system increments by a predefined value upon detection of message loss and decrements by a predefined value when a correctly relayed message is detected. The hysteresis counter system generates an alert signal when a counter threshold is exceeded. The predefined values for incrementing and decrementing may be dynamically adjusted based on network conditions or operational parameters. The hysteresis counter system may be implemented using hardware-based counters, software-driven tracking mechanisms, or integrated digital logic circuits. The hysteresis counter system may be integrated with network monitoring tools to analyze historical message loss trends. The hysteresis counter system may trigger corrective actions, such as adjusting message transmission rates, reallocating network resources, or initiating retransmissions when message loss exceeds acceptable limits. The alert signal generated by the hysteresis counter system may be transmitted to a monitoring system or logged for further analysis.
In an embodiment, a sequential verification mechanism may be operable over multiple time frames. The sequential verification mechanism performs message integrity verification sequentially across subgroups of communication entities 102, optimizing computational resources by distributing verification over distinct time frames. The sequential verification mechanism may be based on predefined time intervals, event-triggered conditions, or adaptive scheduling techniques. The sequential verification mechanism may assign priority levels to different verification tasks, allowing critical messages to be verified first. The sequential verification mechanism may be implemented using parallel processing architectures, distributed computing frameworks, or software-based scheduling algorithms. The sequential verification mechanism may maintain logs of verification results for further analysis. The sequential verification mechanism may operate in real-time or as part of scheduled verification cycles. The sequential verification mechanism may interface with external diagnostic systems to correlate message verification results with network performance metrics.
In an embodiment, the verification unit 108 may be operatively linked to an adaptive loss-tolerance mechanism that adjusts message loss thresholds based on communication network conditions. The adaptive loss-tolerance mechanism dynamically modifies acceptable message loss levels to accommodate variations in network stability. The adaptive loss-tolerance mechanism may utilize statistical models, historical data, or real-time monitoring techniques to determine optimal loss thresholds. The adaptive loss-tolerance mechanism may be implemented using software-based control logic, firmware-integrated routines, or adaptive machine learning frameworks. The adaptive loss-tolerance mechanism may adjust thresholds based on predefined rules, configurable policies, or automated feedback mechanisms. The adaptive loss-tolerance mechanism may interact with network monitoring tools to assess environmental factors affecting message loss, such as signal interference, transmission delays, or congestion. The adaptive loss-tolerance mechanism may generate reports or logs detailing threshold adjustments for future analysis. The adaptive loss-tolerance mechanism may operate continuously or at scheduled intervals.
In an embodiment, the message logging system 104 may support a distributed ledger storage that enables synchronization of message logs across multiple redundant storage units. The distributed ledger storage prevents single-point failures by distributing copies of logged data across multiple locations. The distributed ledger storage may operate using blockchain technology, decentralized database frameworks, or synchronized data replication techniques. The distributed ledger storage may employ cryptographic security measures to prevent unauthorized modifications of stored data. The distributed ledger storage may synchronize message logs in real-time, at predefined intervals, or upon detection of discrepancies. The distributed ledger storage may store message logs in a structured or unstructured format depending on operational requirements. The distributed ledger storage may facilitate auditing, forensic analysis, and long-term record retention of communication logs. The distributed ledger storage may be implemented using local storage devices, cloud-based architectures, or hybrid storage models. The distributed ledger storage may support configurable access control mechanisms.
In an embodiment, a message integrity scoring unit may assign a confidence score to each message based on multiple integrity parameters. The message integrity scoring unit evaluates message paths, cryptographic hash consistency, and transmission reliability factors. The message integrity scoring unit may operate using weighted scoring models, machine learning classifiers, or rule-based evaluation techniques. The message integrity scoring unit may correlate message integrity scores with historical verification data to identify potential anomalies. The message integrity scoring unit may be implemented in hardware-based security modules, software-driven verification frameworks, or embedded computing systems. The message integrity scoring unit may maintain a database of integrity scores for future reference and auditing purposes. The message integrity scoring unit may generate reports summarizing integrity assessments for review by network administrators. The message integrity scoring unit may integrate with anomaly detection systems to identify patterns of compromised message integrity. The message integrity scoring unit may interface with fault localization mechanisms.
In an embodiment, an anomaly response unit may be operatively associated with the verification unit 108 and modifies communication parameters upon detection of recurring patterns of message loss. The anomaly response unit identifies deviations from expected transmission behaviors and applies corrective measures. The anomaly response unit may be implemented using predefined rule-based responses, adaptive machine learning-based adjustments, or feedback-driven control mechanisms. The anomaly response unit may modify transmission parameters such as message retransmission intervals, redundancy levels, or priority settings to mitigate recurring message loss issues. The anomaly response unit may log detected anomalies and applied corrective actions for future analysis. The anomaly response unit may operate in a real-time monitoring mode or as part of scheduled diagnostic routines. The anomaly response unit may interface with other network monitoring tools to assess long-term trends in message loss and adjust parameters accordingly. The anomaly response unit may initiate alerts when severe anomalies are detected.
In an embodiment, an implementation unit may perform online monitoring during flight operations and offline post-flight analysis of message integrity data. The online monitoring process tracks real-time message transmissions using a central communication entity 102. The implementation unit may continuously verify message integrity, detect discrepancies, and log communication anomalies. The offline post-flight analysis process examines collected message logs to identify patterns of transmission errors, assess network performance, and perform predictive maintenance analysis. The implementation unit may utilize cloud-based analytics, onboard computing resources, or external diagnostic tools for offline data processing. The implementation unit may generate reports summarizing communication trends, historical message discrepancies, and network reliability metrics. The implementation unit may store analyzed data for long-term recordkeeping. The implementation unit may correlate in-flight communication anomalies with environmental factors such as weather conditions, signal interference, or system load variations.
In an embodiment, the fault localization unit 110 may be operatively linked to a flight phase mapping unit that correlates message losses with specific aircraft operational phases. The flight phase mapping unit identifies when message integrity issues occur relative to key flight phases such as take off, cruising, or landing. The flight phase mapping unit may use timestamps, sensor data, or recorded flight parameters to determine the correlation between message discrepancies and operational events. The flight phase mapping unit may store historical flight phase data for retrospective analysis. The flight phase mapping unit may integrate with avionics monitoring systems to provide context-aware fault localization. The flight phase mapping unit may analyze message loss distribution patterns over multiple flights to identify trends. The flight phase mapping unit may support automated alerts when message loss rates exceed predefined limits for specific flight phases. The flight phase mapping unit may operate autonomously or as part of a larger diagnostic framework.
In an embodiment, the message logging system 104 may enable cryptographic watermarking of messages to enhance data authenticity and prevent unauthorized modifications. The cryptographic watermarking process embeds verification markers within transmitted messages, allowing validation at any stage of the communication process. The cryptographic watermarking method may involve digital signatures, hash-based authentication codes, or steganographic encoding techniques. The cryptographic watermarking system may operate in real-time or as part of post-transmission validation. The cryptographic watermarking system may generate tamper-evident logs to assure message authenticity. The cryptographic watermarking system may support verification methods based on cryptographic key management systems. The cryptographic watermarking system may be integrated with message integrity scoring mechanisms to assess authenticity confidence levels. The cryptographic watermarking system may store records of validated messages for auditing purposes. The cryptographic watermarking system may prevent replay attacks by associating time-sensitive authentication markers with transmitted messages. The cryptographic watermarking system may support multiple cryptographic standards.
FIG. 2 illustrates a method 200 for tracking and verifying message integrity in an avionics communication network, in accordance with the embodiments of the present disclosure. At step 202, messages are transmitted and received among a plurality of communication entities 102 within an avionics communication network. The communication entities 102 facilitate data exchange between avionics subsystems, flight control systems, navigation units, and monitoring instruments. The communication entities 102 may operate using wired or wireless transmission technologies, affirming continuous data exchange across network nodes. Each message may be assigned an identifier, sequence number, and timestamp to enable tracking and verification. The transmission process may involve direct peer-to-peer messaging or multi-hop relay mechanisms, depending on network topology. Transmission integrity may be maintained using error detection techniques such as cyclic redundancy checks or cryptographic hash verification. Message reception may involve data buffering, decoding, and validation processes to confirm successful delivery to intended recipients.
At step 204, each transmitted and received message is logged using a message logging system 104 operatively associated with each communication entity 102. The message logging system 104 records details such as message content, transmission timestamps, source communication entity 102, and destination communication entity 102. Each log entry comprises a cryptographic hash representing a message path, preventing unauthorized modifications. The message logging system 104 may utilize temporary storage buffers, non-volatile memory, or cloud-based storage solutions to retain message records. Logged data may be indexed using message identifiers and timestamps for efficient retrieval and analysis. Security measures such as encryption and access control mechanisms may be applied to protect stored logs. The message logging system 104 may operate in real-time or batch mode, depending on network conditions and operational requirements. Logged messages may be periodically analyzed to detect anomalies, message loss, or inconsistencies in communication patterns across multiple communication entities 102.
At step 206, a blockchain-based tracking mechanism 106 is formed, interconnecting the communication entities 102. Each transmitted message is recorded as a part of a blockchain-based tracking mechanism 106, assuring message logs remain immutable and verifiable. The blockchain-based tracking mechanism 106 comprises a plurality of blocks, each block linked by a cryptographic hash and containing at least source information, message information, and a unique timestamp. The blockchain-based tracking mechanism 106 may be implemented using a permissioned or private blockchain, preventing unauthorized modifications to stored data. Each message entry is validated using cryptographic techniques to maintain data integrity. Redundancy mechanisms may be employed to prevent message loss and enable continued access to historical communication records. The blockchain-based tracking mechanism 106 may be distributed across multiple storage nodes, enhancing reliability and accessibility. The blockchain-based tracking mechanism 106 may support timestamp-based querying and retrieval of previously transmitted messages for verification and forensic analysis.
At step 208, message logs from the communication entities 102 are collected and compared using a verification unit 108 to identify discrepancies in message integrity. The verification unit 108 retrieves message logs from the message logging system 104 and the blockchain-based tracking mechanism 106 to perform validation checks. The verification unit 108 examines cryptographic hashes, timestamps, and message sequences to determine whether messages were modified, lost, or delayed. The verification unit 108 may conduct real-time or scheduled verification operations, depending on network requirements. The verification unit 108 may detect message duplication, out-of-sequence arrivals, or incomplete transmissions. The verification unit 108 may store validation results for future reference or trigger alerts if discrepancies are detected. The verification unit 108 may interface with external monitoring tools to correlate message verification data with system performance metrics. The verification unit 108 may maintain audit trails of all integrity verification processes.
At step 210, the verification unit 108 performs a comparison between messages sent by a source communication entity 102 and messages received by a destination communication entity 102. The verification unit 108 extracts message logs from both communication entities 102 and evaluates consistency between transmitted and received data. The comparison may involve checking message payloads, sequence numbers, and cryptographic hash values to confirm that received messages match those originally sent. The verification unit 108 may identify cases where messages have been dropped, delayed, or altered during transmission. Comparison results may be logged for further investigation. If discrepancies are detected, the verification unit 108 may generate anomaly reports or trigger additional verification procedures. The verification unit 108 may support configurable tolerance thresholds to account for minor transmission delays. Comparison results may be transmitted to diagnostic systems or stored within the blockchain-based tracking mechanism 106 for further validation.
At step 212, the verification unit 108 analyzes the logs from intermediate communication entities 102 to detect discrepancies and determine the location of message loss. The verification unit 108 traces message transmission paths across multiple communication entities 102, examining timestamps and sequence patterns to pinpoint where anomalies occur. The verification unit 108 may employ statistical analysis techniques to assess network reliability and identify nodes exhibiting frequent message loss. The verification unit 108 may correlate discrepancy reports with network traffic data to differentiate between transient transmission errors and persistent faults. Logged comparison results may be archived for trend analysis and future reference. The verification unit 108 may interface with monitoring tools to provide a real-time view of network integrity. The verification unit 108 may detect recurring loss patterns and recommend corrective actions. The verification unit 108 may log results to support efficient retrieval and analysis.
At step 214, a fault localization unit 110 identifies a communication entity 102 at which message loss occurred based on discrepancies detected in the message logs. The fault localization unit 110 examines message timestamps, sequence tracking data, and cryptographic validation records to determine the specific communication entity 102 where message loss took place. The fault localization unit 110 may utilize automated diagnostic techniques such as network topology mapping or historical anomaly pattern recognition to refine loss detection accuracy. The fault localization unit 110 may generate detailed diagnostic summaries indicating which communication entities 102 exhibit recurrent message loss issues. The fault localization unit 110 may interface with avionics maintenance systems to provide fault location data for targeted troubleshooting. The fault localization unit 110 may store identified fault records within a logging framework to support forensic analysis. The fault localization unit 110 may operate continuously or as part of scheduled network integrity assessments.
In an embodiment, a time-series variation in message transmission across communication entities 102 may be determined using a message propagation analysis unit operatively associated with a verification unit 108. The message propagation analysis unit tracks transmission delays, sequence irregularities, and fluctuations in message delivery rates over time. The message propagation analysis unit may analyze timestamps, sequence numbers, and transmission intervals to detect inconsistencies in message flow. The message propagation analysis unit may store time-series data in logs, facilitating historical trend analysis. The message propagation analysis unit may operate using statistical models, machine learning frameworks, or predefined rule-based assessments to detect anomalies in message propagation. The message propagation analysis unit may correlate time-based message delays with environmental factors, such as network congestion, interference, or hardware failures. The message propagation analysis unit may provide real-time assessments of network stability by continuously monitoring propagation characteristics across multiple communication entities 102.
In an embodiment, symmetric cryptographic functions may be applied to maintain data confidentiality using a lightweight encryption unit incorporated within a blockchain-based tracking mechanism 106. The lightweight encryption unit encrypts messages before transmission and decrypts messages upon reception to prevent unauthorized access. The lightweight encryption unit may utilize symmetric key encryption techniques, including AES, DES, or Blowfish, depending on security requirements. The lightweight encryption unit may integrate with key management systems to regulate encryption key distribution and lifecycle management. The lightweight encryption unit may be optimized for low-latency encryption and decryption, enabling minimal processing overhead during real-time message exchange. The lightweight encryption unit may apply dynamic key rotation mechanisms to periodically update cryptographic keys. The lightweight encryption unit may operate in conjunction with blockchain-based authentication mechanisms to enhance message security. The lightweight encryption unit may support configurable encryption levels based on communication priority, message type, or network conditions. The lightweight encryption unit may implement integrity checks to validate encrypted data before decryption.
In an embodiment, an alternative routing approach may be initiated upon detection of persistent message loss using a self-healing communication mechanism operatively linked to a verification unit 108. The self-healing communication mechanism dynamically identifies alternative communication paths when message loss is detected beyond predefined thresholds. The self-healing communication mechanism may utilize real-time network topology analysis to select optimal rerouting paths. The self-healing communication mechanism may prioritize alternate routes based on latency, bandwidth availability, or historical reliability. The self-healing communication mechanism may apply adaptive retransmission techniques to make sure message delivery under adverse network conditions. The self-healing communication mechanism may operate using predefined routing logic, machine learning-based optimization, or predictive analysis of network behavior. The self-healing communication mechanism may interact with fault localization units to identify communication entities 102 responsible for message disruptions.
In an embodiment, emerging failure trends based on historical message loss data may be identified using an avionics health prediction unit operatively associated with a verification unit 108. The avionics health prediction unit analyzes long-term message loss patterns, identifying communication entities 102 prone to recurrent failures. The avionics health prediction unit may employ statistical forecasting, trend analysis, or machine learning techniques to predict future failure occurrences. The avionics health prediction unit may categorize failure patterns based on severity, frequency, and affected network segments. The avionics health prediction unit may store historical failure records for trend comparison and predictive analytics. The avionics health prediction unit may correlate message loss trends with system maintenance logs, environmental conditions, or operational loads. The avionics health prediction unit may generate early warnings for preventive maintenance actions, reducing potential system downtimes. The avionics health prediction unit may provide continuous monitoring and adaptation based on newly acquired failure data.
In an embodiment, message discrepancies may be categorized based on severity and recurrence using an artificial intelligence-based classification engine operatively associated with a verification unit 108. The artificial intelligence-based classification engine applies machine learning algorithms, decision trees, or clustering techniques to analyze detected discrepancies. The artificial intelligence-based classification engine distinguishes between transient message errors, systematic communication failures, and potential security threats. The artificial intelligence-based classification engine may assign priority levels to discrepancies based on impact assessments and frequency analysis. The artificial intelligence-based classification engine may store classification results in logs for further investigation. The artificial intelligence-based classification engine may interface with network diagnostic tools to refine classification accuracy. The artificial intelligence-based classification engine may dynamically update classification models based on newly observed message discrepancy patterns.
In an embodiment, critical message integrity events may be logged across multiple storage nodes using a decentralized event logging unit, reducing single-point failure risks. The decentralized event logging unit distributes message integrity logs across geographically dispersed or networked storage locations. The decentralized event logging unit may operate using blockchain-based recordkeeping, cloud-based distributed databases, or peer-to-peer data synchronization mechanisms. The decentralized event logging unit may implement cryptographic authentication methods to secure logged data. The decentralized event logging unit may synchronize logs periodically or in real-time, depending on system configuration. The decentralized event logging unit may store event records in immutable formats to prevent unauthorized modifications. The decentralized event logging unit may facilitate rapid retrieval of event logs by indexing records based on timestamps, message sources, and detected discrepancies. The decentralized event logging unit may support access control mechanisms, making sure that only authorized entities retrieve and analyze integrity event records.
In an embodiment, missing message content may be estimated based on previously transmitted messages using a real-time data reconstruction unit operatively linked to a fault localization unit 110. The real-time data reconstruction unit applies predictive modeling, statistical inference, or pattern-based reconstruction methods to recover lost message content. The real-time data reconstruction unit may analyze historical message sequences to infer probable missing data. The real-time data reconstruction unit may utilize error correction techniques such as forward error correction, interpolation, or approximation algorithms to reconstruct incomplete messages. The real-time data reconstruction unit may compare partially received messages against stored message templates to estimate missing segments. The real-time data reconstruction unit may validate reconstructed data before retransmission or storage. The real-time data reconstruction unit may operate in real-time or batch-processing modes, depending on system requirements. The real-time data reconstruction unit may generate reconstruction confidence scores indicating the reliability of recovered message content.
In an embodiment, cryptographic keys may be updated periodically to enhance security against data breaches using a secure key rotation unit incorporated within a blockchain-based tracking mechanism 106. The secure key rotation unit applies scheduled or event-driven key updates, preventing prolonged key exposure risks. The secure key rotation unit may utilize key distribution protocols, encryption key hierarchies, or cryptographic key renewal techniques to replace outdated keys. The secure key rotation unit may integrate with blockchain-based authentication mechanisms to validate newly assigned keys. The secure key rotation unit may enforce automatic revocation of compromised or expired keys. The secure key rotation unit may maintain access logs detailing key rotation events for auditing purposes. The secure key rotation unit may implement access control policies restricting unauthorized key modifications.
In an embodiment, the plurality of communication entities 102 transmit and receive messages within an avionics communication network, assuring continuous data exchange between avionics subsystems. Communication entities 102 operate using wired or wireless transmission techniques, supporting bidirectional communication with minimal latency. Communication entities 102 may implement error detection mechanisms, including cyclic redundancy checks and parity validation, reducing the likelihood of undetected transmission errors. Communication entities 102 may prioritize critical control messages over routine data transmissions, affirming time-sensitive operations are not delayed. Synchronization among communication entities 102 may be achieved using timestamping mechanisms, preventing sequencing errors and improving message consistency. Communication entities 102 may dynamically adjust transmission paths based on network congestion, preventing data bottlenecks. Logged message transactions may be analyzed to detect patterns of inconsistent transmissions, supporting predictive maintenance and improving long-term network reliability.
In an embodiment, the message logging system 104 is operatively associated with each communication entity 102, recording transmitted and received messages for verification purposes. Message logging system 104 generates cryptographic hash values for logged messages, assuring authenticity and preventing unauthorized modifications. Message logging system 104 may store logs to enable efficient retrieval for verification and auditing. Message logging system 104 may synchronize stored logs across multiple redundant storage units, preventing data loss in the event of hardware failure. Message logging system 104 may encrypt logged records, restricting unauthorized access to stored message data. Message logging system 104 may support automated indexing based on timestamps, message sequence numbers, and communication entities 102, improving retrieval efficiency. Logged data may be periodically analyzed to detect anomalies, such as missing or altered transmissions, enabling proactive network stability assessments.
In an embodiment, the blockchain-based tracking mechanism 106 interconnects communication entities 104, recording transmitted messages as blockchain transactions. Blockchain-based tracking mechanism 106 stores message transactions in an immutable sequence of blocks, preventing unauthorized modifications to historical message records. Blockchain-based tracking mechanism 106 may synchronize across multiple network nodes, assuring continuous access to message logs without dependency on a centralized repository. Blockchain-based tracking mechanism 106 may apply cryptographic validation techniques, verifying message authenticity before appending records to the blockchain. Blockchain-based tracking mechanism 106 may support time-indexed queries, allowing rapid retrieval of historical message records.
In an embodiment, the verification unit 108 collects and compares message logs from communication entities 102, detecting transmission inconsistencies. Verification unit 108 performs a comparison between messages sent by a source communication entity 102 and messages received by a destination communication entity 102, identifying missing, delayed, or modified transmissions. Verification unit 108 may analyze timestamps, cryptographic hash values, and message sequence patterns, detecting discrepancies with high accuracy. Verification unit 108 may store comparison results, allowing retrospective network performance assessments. Verification unit 108 may generate alerts upon detecting message discrepancies exceeding predefined thresholds, supporting real-time anomaly detection. Verification unit 108 may interface with security monitoring tools, preventing unauthorized message modifications. Verification unit 108 may support scheduled or continuous verification cycles, adapting to varying network conditions.
In an embodiment, the fault localization unit 110 identifies a communication entity 102 responsible for message loss, supporting targeted troubleshooting efforts. Fault localization unit 110 traces message transmission paths, pinpointing the exact location of message loss within the network. Fault localization unit 110 may analyze timestamp variations, identifying points of failure with high accuracy. Fault localization unit 110 may generate diagnostic summaries, indicating which communication entities 102 exhibit recurring message loss. Fault localization unit 110 may correlate detected failures with environmental conditions, determining whether signal interference or system transitions contribute to transmission inconsistencies. Fault localization unit 110 may interface with predictive maintenance tools, allowing preemptive corrective actions to prevent future message discrepancies.
In an embodiment, the hysteresis counter system is operatively linked to a verification unit 108, tracking message loss occurrences over time. Hysteresis counter system increments a counter when message loss is detected and decrements the counter when a correctly relayed message is received. Hysteresis counter system generates an alert when the counter exceeds a predefined threshold, preventing unnecessary alerts due to minor fluctuations. Hysteresis counter system may dynamically adjust predefined increment and decrement values, improving sensitivity to network conditions. Hysteresis counter system may store historical counter values, facilitating long-term reliability assessments of communication entities 102.
In an embodiment, the sequential verification mechanism performs message integrity verification over multiple time frames, distributing verification processes across subgroups of communication entities 102. Sequential verification mechanism reduces computational workload by staggering verification tasks, optimizing system resources. Sequential verification mechanism may prioritize high-risk messages for early validation, improving the detection of discrepancies. Sequential verification mechanism may store verification results, enabling retrospective analysis of message integrity over extended time periods.
In an embodiment, the adaptive loss-tolerance mechanism is operatively linked to the verification unit 108, adjusting message loss thresholds dynamically based on communication network conditions. Adaptive loss-tolerance mechanism may modify acceptable loss rates in response to fluctuating network stability, reducing false anomaly detections. Adaptive loss-tolerance mechanism may integrate with message propagation analysis tools, refining message loss evaluations. Adaptive loss-tolerance mechanism may store loss threshold adjustments, supporting long-term trend analysis of network resilience.
In an embodiment, the anomaly response unit is operatively associated with the verification unit 108, modifying communication parameters upon detection of recurring message loss patterns. Anomaly response unit may adjust retransmission intervals, redundancy levels, or priority settings to optimize network stability. Anomaly response unit may interact with fault localization unit 110 to determine affected communication entities 102, applying targeted corrective measures.
In an embodiment, the avionics health prediction unit analyzes historical message loss data, identifying failure trends that impact network stability. Avionics health prediction unit may generate predictive reports, assisting in proactive maintenance scheduling. Avionics health prediction unit may interface with self-healing communication mechanisms, dynamically rerouting messages when failure probabilities exceed predefined thresholds.
In an embodiment, the decentralized event logging unit logs critical message integrity events across multiple storage nodes, preventing single-point failures in message retention. Decentralized event logging unit distributes logged records across geographically dispersed storage locations. Decentralized event logging unit may apply blockchain-based storage techniques, preventing unauthorized modifications to stored logs.
In an embodiment, cryptographic watermarking is applied to messages using message logging system 104, embedding verification markers to make sure authenticity. Cryptographic watermarking prevents unauthorized message alterations, improving data integrity. Cryptographic watermarking may support timestamp-based validation, detecting unauthorized modifications over extended periods. Cryptographic watermarking may integrate with blockchain-based tracking mechanism 106, affirming long-term authenticity verification.
In an embodiment, the secure key rotation unit updates cryptographic keys periodically, preventing prolonged key exposure risks. Secure key rotation unit may automate key renewal cycles, maintaining continuous security compliance. Secure key rotation unit may integrate with authentication frameworks, validating newly assigned cryptographic keys before deployment. Secure key rotation unit may store key rotation logs, supporting auditing requirements.
FIG. 3 illustrates a data flow diagram of message integrity tracking and verification in an avionics communication network, in accordance with the embodiments of the present disclosure. A communication entity 102 transmits and receives messages as part of network communication. Upon transmission or reception, a message logging system 104 logs each message for verification purposes. Message logging system 104 generates a cryptographic hash for each message and stores each generated cryptographic hash in a blockchain ledger, affirming immutability and security. Simultaneously, message logging system 104 records log entries in verification logs, maintaining a record of transmitted and received messages. Verification logs are retrieved and compared by a verification unit 108 to identify potential discrepancies. Verification unit 108 detects inconsistencies between sent and received messages, forwarding detected anomalies to a discrepancy detection mechanism. Upon detecting a discrepancy, discrepancy detection initiates an analysis process to determine message loss or modification. Fault localization unit 110 receives discrepancy data and analyzes transmission paths to identify the specific communication entity 102 responsible for message loss or alteration. Fault localization unit 110 enables targeted troubleshooting and corrective actions by pinpointing the exact location of faults within the network.
FIG. 4 illustrates a message integrity verification workflow in an avionics communication network, in accordance with the embodiments of the present disclosure. Message transmission and logging are initiated, wherein transmitted messages are recorded and stored. Blockchain-based tracking and verification are performed to create an immutable record of each message, enabling traceability. Source and destination messages are then compared to determine integrity. If the messages match, message integrity is verified, confirming successful transmission without alterations. If a mismatch is detected, fault localization is performed to identify the communication entity responsible for message discrepancies. Discrepancy analysis is conducted to determine the cause of message loss, modification, or corruption. Upon identifying the issue, corrective actions are implemented to restore network reliability.
In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “disposed,” “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Modifications to embodiments and combination of different embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “comprising”, “comprising”, “incorporating”, “have”, “is” used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.
Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
,CLAIMS:WE CLAIM:
1. A system 100 to track and verify message integrity in an avionics communication network, comprising:
a plurality of communication entities 102 transmitting and receiving the messages;
a message logging system 104 operatively associated with each communication entity 102, the message logging system 104 configured to log each transmitted and received message, wherein each log comprises a cryptographic hash representing a message path from a source communication entity 102 to a destination communication entity 102;
a blockchain-based tracking mechanism 106 interconnecting the communication entities 104, wherein each transmitted message is a part of a blockchain, the blockchain comprising a plurality of blocks, each block is linked by the cryptographic hash and comprising at least source information, message information, and a unique timestamp;
a verification unit 108 configured to collect and compare the message logs from the communication entities 102, wherein the verification unit 108 performs:
a comparison between the messages sent by the source communication entity 102 and the messages received by the destination communication entity 102;
an analysis of the logs from the intermediate communication entities 102 to detect the discrepancies and determine the location of message loss;
a fault localization unit 110 configured to identify a communication entity 102 at which the message loss occurred based on the discrepancies detected in the message logs.
2. The system 100 of claim 1, further comprising a hysteresis counter system that is operatively linked to the verification unit 108, wherein the hysteresis counter system:
increments by a predefined value upon detection of the message loss;
decrements by a predefined value upon detection of a correctly relayed message; and
generates an alert signal when a counter threshold is exceeded.
3. The system 100 of claim 1, further comprising a sequential verification module that is operable over multiple time frames, wherein the sequential verification module:
performs message integrity verification sequentially across the subgroups of the communication entities 102;
optimizes the computational resources by distributing verification over the distinct time frames.
4. The system 100 of claim 1, wherein the verification unit 108 is operatively linked to an adaptive loss-tolerance mechanism, wherein the adaptive loss-tolerance mechanism adjusts the message loss thresholds based on the communication network conditions.
5. The system 100 of claim 1, wherein the message logging system 104 is configured to support a distributed ledger storage, wherein the distributed ledger storage enables synchronization of the message logs across multiple redundant storage units.
6. The system 100 of claim 1, further comprising a message integrity scoring unit that is configured to assign a confidence score to each message, wherein the message integrity scoring unit determines reliability of the communication paths.
7. The system 100 of claim 1, further comprising an anomaly response unit that is operatively associated with the verification unit 108, wherein the anomaly response unit modifies the communication parameters upon detection of the recurring patterns of the message loss.
8. The system 100 of claim 1, further comprising an implementation unit that performs:
online monitoring during flight, wherein real-time message integrity tracking is performed using a central communication entity 102; and
offline post-flight analysis, wherein the collected message logs are analyzed for predictive maintenance and dormant failure detection.
9. The system 100 of claim 1, wherein the fault localization unit 110 is operatively linked to a flight phase mapping unit, wherein the flight phase mapping unit correlates the message losses with the specific aircraft operational phases.
10. The system 100 of claim 1, wherein the message logging system 104 is configured to enable cryptographic watermarking of the messages, wherein the cryptographic watermarking enhances data authenticity and prevents unauthorized modifications.
11. A method 200 for tracking and verifying message integrity in an avionics communication network, comprising:
transmitting and receiving the messages among a plurality of communication entities 102;
logging each transmitted and received message using a message logging system 104 operatively associated with each communication entity 102, wherein each log comprises a cryptographic hash representing a message path from a source communication entity 102 to a destination communication entity 102;
forming a blockchain-based tracking mechanism 106 interconnecting the communication entities 102, wherein each transmitted message forms a part of a blockchain, the blockchain comprising a plurality of blocks, each block linked by a cryptographic hash and comprising at least source information, message information, and a unique timestamp;
collecting and comparing the message logs from the communication entities 102 using a verification unit 108, wherein the verification unit 108 performs:
a comparison between the messages sent by the source communication entity 102 and the messages received by the destination communication entity 102;
an analysis of the logs from the intermediate communication entities 102 to detect the discrepancies and determine the location of message loss; and
identifying a communication entity 102 at which the message loss occurred using a fault localization unit 110, wherein the identification is based on the discrepancies detected in the message logs.
12. The method 200 of claim 11, further comprising determining the time-series variations in message transmission across the communication entities 102 using a message propagation analysis unit operatively associated with the verification unit 108.
13. The method 200 of claim 11, further comprising applying the symmetric cryptographic functions to maintain data confidentiality using a lightweight encryption unit incorporated within the blockchain-based tracking mechanism 106.
14. The method 200 of claim 11, further comprising initiating an alternative routing protocol upon detection of persistent message loss using a self-healing communication mechanism operatively linked to the verification unit 108.
15. The method 200 of claim 11, further comprising identifying the emerging failure trends based on historical message loss data using an avionics health prediction unit operatively associated with the verification unit 108.
16. The method 200 of claim 11, further comprising categorizing the message discrepancies based on severity and recurrence using an artificial intelligence-based classification engine operatively associated with the verification unit 108.
17. The method 200 of claim 11, further comprising logging the critical message integrity events across multiple storage nodes using a decentralized event logging unit, wherein the decentralized event logging reduces the single-point failure risks.
18. The method 200 of claim 11, further comprising estimating missing message content based on previously transmitted messages using a real-time data reconstruction unit operatively linked to the fault localization unit 110.
19. The method 200 of claim 11, further comprising updating the cryptographic keys periodically to enhance security against data breaches using a secure key rotation unit incorporated within the blockchain-based tracking mechanism 106.