Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to the field of secure and intelligent trust management in vehicular ad-hoc networks (VANETs) and edge computing environments. More specifically, it pertains to a quantum-resistant federated trust orchestration system for dynamic edge-VANET environments.
BACKGROUND OF THE DISCLOSURE
[0002] Vehicular Ad Hoc Networks (VANETs) have emerged as a critical technological paradigm in the development of intelligent transportation systems. With the rapid proliferation of connected vehicles, roadside units, and dynamic communication infrastructures, VANETs serve as the backbone for enabling real-time vehicular communication, safety alerts, traffic management, and infotainment services. Unlike traditional static networks, VANETs are highly dynamic, as vehicles constantly join and leave the network, which introduces significant challenges in maintaining stable, secure, and reliable communication. As edge computing has gained prominence in recent years, it has further extended VANET functionality by reducing latency, enabling distributed data processing, and facilitating collaborative decision-making at the edge rather than relying entirely on centralized cloud infrastructures.
[0003] Security and trust management have always been pivotal concerns within VANETs due to the highly decentralized nature of the network. Malicious entities, compromised nodes, and faulty communications can severely undermine the safety and efficiency of vehicular communication systems. Early approaches to securing VANETs primarily relied on centralized certificate authorities and cryptographic primitives to ensure message integrity and authentication. However, these centralized solutions often suffered from scalability issues and latency overhead, which are unacceptable in high-mobility vehicular scenarios where real-time decisions are crucial. To overcome these limitations, researchers explored distributed trust models and blockchain-inspired decentralized solutions. While these methods improved resilience and transparency, they also introduced computational burdens and communication delays that are difficult to manage in dynamic, resource-constrained edge environments.
[0004] The rise of quantum computing has introduced an additional layer of complexity in designing trust and security mechanisms for vehicular communication systems. Classical cryptographic protocols, including widely used public-key algorithms such as RSA and ECC, are vulnerable to attacks from sufficiently powerful quantum computers. This impending threat has necessitated the development of post-quantum cryptography and quantum-resistant trust models to ensure the long-term viability of secure VANET infrastructures. The transition from classical to quantum-resistant cryptographic schemes requires careful balancing of computational efficiency, communication overhead, and energy consumption, particularly in vehicular and edge contexts where resources are limited and mobility is continuous.
[0005] Federated learning and federated trust models have also emerged as powerful paradigms in distributed systems, including VANETs. Federated approaches allow multiple entities to collaboratively build and refine trust models without the need to share sensitive raw data, thereby preserving privacy while enabling more robust decision-making. By distributing trust evaluation and orchestration across edge devices and vehicular units, federated systems reduce the dependency on centralized authorities while maintaining adaptability in highly dynamic environments. However, federated trust models are not without challenges. Issues such as model poisoning, adversarial manipulation, synchronization overhead, and unequal participation of nodes remain persistent obstacles. Additionally, federated architectures must be carefully designed to handle heterogeneous data sources, intermittent connectivity, and varying computational capacities across nodes.
[0006] Dynamic orchestration of trust mechanisms within edge-based VANETs is essential to address the real-time nature of vehicular communication. Unlike static or semi-static trust models, orchestration frameworks must continuously adapt to changing network topologies, fluctuating node densities, and diverse threat landscapes. This requires a delicate integration of lightweight cryptographic schemes, adaptive consensus mechanisms, and intelligent trust evaluation strategies that can function effectively under strict latency and mobility constraints. In particular, achieving scalability without compromising trustworthiness or introducing excessive computational delays has been a long-standing challenge in the design of next-generation vehicular communication systems.
[0007] The intersection of edge computing, federated trust, and quantum-resistant security mechanisms forms a rich research landscape that continues to evolve. The ability to seamlessly integrate post-quantum cryptography with federated orchestration frameworks in highly dynamic vehicular environments holds significant potential for the advancement of secure, reliable, and efficient VANETs. Nonetheless, there remain critical gaps in existing solutions, particularly regarding interoperability across heterogeneous vehicular infrastructures, sustainability of resource usage, and resilience against both classical and quantum-enabled adversaries.
[0008] Thus, in light of the above-stated discussion, there exists a need for a quantum-resistant federated trust orchestration system for dynamic edge-VANET environments.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on a quantum-resistant federated trust orchestration system for dynamic edge-VANET environments which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to develop a quantum-resistant security layer that safeguards trust computation and data exchange processes against potential vulnerabilities posed by emerging quantum computing attacks.
[0012] Another objective of the present disclosure is to design a federated trust orchestration framework that dynamically adapts to evolving attacker behaviors such as Grayhole and Sybil attacks, thereby ensuring robust trust management in highly mobile Vehicular Ad-Hoc Network (VANET) environments.
[0013] Another objective of the present disclosure is to replace static machine learning models with adaptive learning mechanisms capable of capturing real-time trust fluctuations across edge devices and vehicles in dynamic network topologies.
[0014] Another objective of the present disclosure is to decentralize trust storage using lightweight distributed techniques that prevent tampering and single-point failures, while avoiding the latency and energy inefficiencies associated with traditional blockchain frameworks.
[0015] Another objective of the present disclosure is to integrate edge computing and federated learning approaches for collaborative trust evaluation, reducing reliance on centralized entities and preserving system scalability in VANETs.
[0016] Another objective of the present disclosure is to ensure strict Quality of Service (QoS) requirements by minimizing computational overhead, communication delays, and energy consumption while maintaining high security and trust accuracy.
[0017] Another objective of the present disclosure is to design a secure trust orchestration protocol that balances transparency, resilience, and privacy, enabling vehicles and roadside units to make real-time trust-based decisions.
[0018] Another objective of the present disclosure is to evaluate the robustness of the system against advanced adversarial models, including adaptive and colluding attackers, in both simulation and real-world vehicular scenarios.
[0019] Another objective of the present disclosure is to enhance interoperability of trust management mechanisms across heterogeneous edge devices, vehicular nodes, and communication standards, ensuring wide adoption and seamless deployment.
[0020] Yet another objective of the present disclosure is to provide a scalable and sustainable trust management architecture that can evolve with future vehicular technologies, including autonomous driving systems and smart transportation infrastructures.
[0021] In light of the above, a quantum-resistant federated trust orchestration system for dynamic edge-VANET environments comprises a post-quantum blockchain module configured to secure trust transactions against quantum attacks using lattice-based cryptography. The system also includes a federated graph neural network module configured to collaboratively train lightweight graph neural network models for representing and predicting trust relationships. The system also includes a multi-agent reinforcement learning (MARL) module configured to autonomously optimize routing decisions based on a trust-utility reward function. The system also includes an adaptive learning rate unit configured to adjust routing policies based on node velocity so as to prioritize stable routes in dynamic topologies. The system also includes a self-healing trust management engine configured to autonomously detect and mitigate malicious behaviors.
[0022] In one embodiment, the post-quantum blockchain module employs Kyber-1024 lattice-based cryptography for key encapsulation and digital signatures to provide resistance against quantum adversaries.
[0023] In one embodiment, the post-quantum blockchain module further comprises a hybrid consensus mechanism combining Proof of Work (PoW) for decentralization and Practical Byzantine Fault Tolerance (PBFT) for low latency, thereby enabling real-time trust updates in VANET environments.
[0024] In one embodiment, the federated graph neural network module integrates a social-aware attention mechanism configured to weight node interactions based on contextual metrics including encounter frequency, mobility patterns, and role hierarchy.
[0025] In one embodiment, the federated graph neural network module generates lightweight GNN embeddings to represent trust relationships, thereby reducing computational complexity for resource-constrained edge devices.
[0026] In one embodiment, the adaptive learning rate unit is configured to increase learning rates under high node velocity conditions to accelerate convergence of routing policies, and to decrease learning rates under low velocity conditions to enhance route stability.
[0027] In one embodiment, the self-healing trust management engine is configured to detect and mitigate Grayhole attacks, Blackhole attacks, and Bad-mouthing attacks by dynamically recalibrating trust thresholds and rerouting communication flows.
[0028] In one embodiment, the self-healing trust management engine further employs time-decay long short-term memory (LSTM) models to diminish the influence of outdated interactions in trust evaluations.
[0029] In one embodiment, the self-healing trust management engine performs reputation consistency checks across nodes and layers to prevent collusion-based manipulation of trust scores.
[0030] In one embodiment, the system further comprises smart contracts deployed within the blockchain to automate enforcement of trust rules, validation of trust scores, and initiation of countermeasures upon detection of malicious activities.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a flowchart outlining sequential step involved in a quantum-resistant federated trust orchestration system for dynamic edge-VANET environments, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a flowchart showing working of a quantum-resistant federated trust orchestration system for dynamic edge-VANET environments, in accordance with an exemplary embodiment of the present disclosure.
[0038] Like reference, numerals refer to like parts throughout the description of several views of the drawing;
[0039] The quantum-resistant federated trust orchestration system for dynamic edge-VANET environments, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0040] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to 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 spirit and scope of the present disclosure.
[0041] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0042] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0043] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0044] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0045] Referring now to FIG. 1 to FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a flowchart outlining sequential step involved in a quantum-resistant federated trust orchestration system for dynamic edge-VANET environments, in accordance with an exemplary embodiment of the present disclosure.
[0046] A quantum-resistant federated trust orchestration system for dynamic edge-VANET environments 100 comprises a post-quantum blockchain module 102 configured to secure trust transactions against quantum attacks using lattice-based cryptography. The post-quantum blockchain module 102 employs Kyber-1024 lattice-based cryptography for key encapsulation and digital signatures to provide resistance against quantum adversaries. The post-quantum blockchain module 102 further comprises a hybrid consensus mechanism combining Proof of Work (PoW) for decentralization and Practical Byzantine Fault Tolerance (PBFT) for low latency, thereby enabling real-time trust updates in VANET environments.
[0047] The system also includes a federated graph neural network module 104 configured to collaboratively train lightweight graph neural network models for representing and predicting trust relationships. The federated graph neural network module 104 integrates a social-aware attention mechanism configured to weight node interactions based on contextual metrics including encounter frequency, mobility patterns, and role hierarchy. The federated graph neural network module 104 generates lightweight GNN embeddings to represent trust relationships, thereby reducing computational complexity for resource-constrained edge devices.
[0048] The system also includes a multi-agent reinforcement learning (MARL) module 106 configured to autonomously optimize routing decisions based on a trust-utility reward function.
[0049] The system also includes an adaptive learning rate unit 108 configured to adjust routing policies based on node velocity so as to prioritize stable routes in dynamic topologies. The adaptive learning rate unit 108 is configured to increase learning rates under high node velocity conditions to accelerate convergence of routing policies, and to decrease learning rates under low velocity conditions to enhance route stability.
[0050] The system also includes a self-healing trust management engine 110 configured to autonomously detect and mitigate malicious behaviors. The self-healing trust management engine 110 is configured to detect and mitigate Grayhole attacks, Blackhole attacks, and Bad-mouthing attacks by dynamically recalibrating trust thresholds and rerouting communication flows. The self-healing trust management engine 110 further employs time-decay long short-term memory (LSTM) models to diminish the influence of outdated interactions in trust evaluations. The self-healing trust management engine 110 performs reputation consistency checks across nodes and layers to prevent collusion-based manipulation of trust scores.
[0051] The system also includes smart contracts deployed within the blockchain to automate enforcement of trust rules, validation of trust scores, and initiation of countermeasures upon detection of malicious activities.
[0052] FIG. 1 illustrates a flowchart outlining sequential step involved in a quantum-resistant federated trust orchestration system for dynamic edge-VANET environments.
[0053] At 102, the process begins with the post-quantum blockchain module. This module plays a foundational role by ensuring that all trust-related transactions, such as updates to node reputations, verifications of communication integrity, and consensus agreements among participants, are secured against adversaries with access to quantum computational power. Unlike traditional cryptographic methods that may become vulnerable in the post-quantum era, this module employs lattice-based cryptography, specifically algorithms such as Kyber-1024, which are resistant to quantum attacks. Furthermore, the blockchain operates under a hybrid consensus mechanism that combines Proof of Work (PoW) for decentralization with Practical Byzantine Fault Tolerance (PBFT) for low-latency transaction validation. By integrating these features, the blockchain not only secures the trust ledger but also maintains real-time responsiveness suitable for VANET environments where vehicles interact and disengage rapidly.
[0054] At 104, above this foundational layer operates the federated graph neural network module, which provides a distributed intelligence framework for modeling and predicting trust relationships. Edge devices in the VANET environment generate local trust data from their firsthand interactions, such as packet forwarding behavior, communication reliability, and contextual social metrics like encounter frequency. Instead of sharing raw sensitive data, these devices train lightweight graph neural network (GNN) models locally. Using a federated learning approach, the models are aggregated across the network using secure multi-party computation, preserving privacy while still enabling collaborative training. The GNN embeddings effectively capture both direct and indirect trust relationships, ensuring that the trust predictions are context-aware and reflective of real-time social and vehicular interactions. This step allows the system to evolve and refine its trust models continuously without exposing raw vehicular data to potential breaches.
[0055] At 106, the trust predictions generated by the federated GNN feed into the multi-agent reinforcement learning (MARL) module, which forms the decision-making intelligence of the system. In this module, each node in the VANET acts as an agent that learns to optimize its routing decisions based on both trustworthiness and network performance.
[0056] At 108, the decision-making process of the MARL module is further enhanced by the adaptive learning rate unit. This unit ensures that routing policies remain aligned with the dynamic mobility patterns of vehicular environments. As node velocities change for instance, when vehicles accelerate on highways or slowdown in congested areas the stability of routes can vary significantly. The adaptive learning rate unit adjusts the speed and sensitivity of the learning process, ensuring that routing decisions prioritize more stable and predictable routes in high-mobility scenarios while allowing for exploration of alternative paths in slower or denser traffic environments. This velocity-aware adjustment makes the routing process highly adaptive and resilient to the frequent topological change’s characteristic of VANETs.
[0057] At 110, the self-healing trust management engine acts as the immune system of the framework. This engine continuously monitors the network for anomalous or malicious behaviors such as Grayhole attacks, where nodes selectively drop packets, or Bad-mouthing attacks, where false trust values are propagated. Upon detection of suspicious patterns, the self-healing engine initiates corrective actions such as recalibrating trust scores, reconfiguring routing strategies, and alerting the network to isolate compromised nodes. The self-healing process is autonomous, meaning it does not rely on centralized intervention, thereby enhancing the scalability and robustness of the system. Moreover, it ensures continuity of operations even in the presence of sophisticated and coordinated adversarial attacks.
[0058] FIG. 2 illustrates a flowchart showing working of a quantum-resistant federated trust orchestration system for dynamic edge-VANET environments.
[0059] At the top, the cloud layer is responsible for policy-level intelligence and global orchestration. Here, the Multi-Agent Reinforcement Learning (MARL) Policy Engine operates to determine adaptive trust thresholds and velocity-aware routing policies. The presence of velocity-aware adaptation ensures that routing decisions remain optimal even when vehicular nodes are highly mobile, which is a frequent challenge in VANETs. Trust thresholds are dynamically recalibrated to respond to variations in node behavior and environmental conditions. The cloud essentially functions as the strategic decision-making layer, setting overarching policies that trickle down into the more localized operations at the edge and fog levels.
[0060] Moving downward, the edge layer consists of individual edge devices such as vehicular nodes, roadside units, and other localized computing entities. Each of these nodes runs TinyML models to facilitate on-device computation with low latency and minimal resource usage. These models help process local trust data, which includes metrics from firsthand interactions, neighbor behavior, and historical communication patterns. In addition to raw trust data, the system incorporates social metrics, such as encounter frequency and role hierarchy, to provide contextual awareness. For instance, frequent encounters between nodes reinforce stronger trust bonds, while the role of a node (e.g., emergency vehicle vs. regular vehicle) may affect its trust weighting. This data is then transformed into lightweight graph neural network (GNN) embeddings, allowing the representation of trust relationships in a scalable, privacy-preserving manner that captures both direct and indirect interactions.
[0061] The fog layer integrates trust computation and consensus mechanisms at an intermediate level between the cloud and edge. Within this layer, federated aggregation via Secure Multi-Party Computation (SMPC) ensures that trust-related models and updates can be collaboratively trained across multiple edge devices without exposing sensitive raw data. This federated learning approach balances privacy with collective intelligence, enabling trust models to continuously improve as they aggregate insights from distributed sources. The fog layer also hosts the post-quantum blockchain infrastructure, which secures trust transactions against quantum-era cryptographic threats. The blockchain is fortified with lattice-based cryptography (Kyber-1024) and employs a hybrid consensus mechanism that combines Proof of Work (PoW) for decentralization with Practical Byzantine Fault Tolerance (PBFT) for low latency and rapid finality. Together, these elements create a trust ledger that guarantees immutability, verifiability, and quantum resistance.
[0062] Complementing the blockchain are smart contracts, which serve as automated enforcers of trust policies and conditions. They enable decentralized execution of rules for node validation, trust updates, and anomaly detection without requiring centralized authority. The fog layer also includes a monitoring mechanism that generates attack alerts whenever malicious patterns such as Grayhole or Bad-mouthing behaviors are detected. These alerts are passed into the self-healing module, which forms a critical resilience feature of the system. The self-healing module dynamically adjusts routing strategies, recalibrates trust values, and initiates countermeasures to mitigate ongoing threats. This not only ensures continuity of secure communication but also allows the system to learn and adapt against evolving attack strategies.
[0063] Supporting these operations are auxiliary components like Time-Decay LSTM models and reputation consistency checks, which continuously validate historical trust scores over time. The time-decay mechanism ensures that outdated interactions carry diminishing influence, keeping the trust model aligned with recent behaviors. Reputation consistency checks validate that trust updates remain coherent across nodes and layers, thereby preventing reputation manipulation or collusion attacks.
[0064] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0065] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0066] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0067] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0068] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A quantum-resistant federated trust orchestration system for dynamic edge-VANET environments (100) comprising:
a post-quantum blockchain module (102) configured to secure trust transactions against quantum attacks using lattice-based cryptography;
a federated graph neural network module (104) configured to collaboratively train lightweight graph neural network models for representing and predicting trust relationships;
a multi-agent reinforcement learning (MARL) module (106) configured to autonomously optimize routing decisions based on a trust-utility reward function;
an adaptive learning rate unit (108) configured to adjust routing policies based on node velocity so as to prioritize stable routes in dynamic topologies;
a self-healing trust management engine (110) configured to autonomously detect and mitigate malicious behaviors.
2. The system (100) as claimed in claim 1, wherein the post-quantum blockchain module (102) employs Kyber-1024 lattice-based cryptography for key encapsulation and digital signatures to provide resistance against quantum adversaries.
3. The system (100) as claimed in claim 1, wherein the post-quantum blockchain module (102) further comprises a hybrid consensus mechanism combining Proof of Work (PoW) for decentralization and Practical Byzantine Fault Tolerance (PBFT) for low latency, thereby enabling real-time trust updates in VANET environments.
4. The system (100) as claimed in claim 1, wherein the federated graph neural network module (104) integrates a social-aware attention mechanism configured to weight node interactions based on contextual metrics including encounter frequency, mobility patterns, and role hierarchy.
5. The system (100) as claimed in claim 1, wherein the federated graph neural network module (104) generates lightweight GNN embeddings to represent trust relationships, thereby reducing computational complexity for resource-constrained edge devices.
6. The system (100) as claimed in claim 1, wherein the adaptive learning rate unit (108) is configured to increase learning rates under high node velocity conditions to accelerate convergence of routing policies, and to decrease learning rates under low velocity conditions to enhance route stability.
7. The system (100) as claimed in claim 1, wherein the self-healing trust management engine (110) is configured to detect and mitigate Grayhole attacks, Blackhole attacks, and Bad-mouthing attacks by dynamically recalibrating trust thresholds and rerouting communication flows.
8. The system (100) as claimed in claim 1, wherein the self-healing trust management engine (110) further employs time-decay long short-term memory (LSTM) models to diminish the influence of outdated interactions in trust evaluations.
9. The system (100) as claimed in claim 1, wherein the self-healing trust management engine (110) performs reputation consistency checks across nodes and layers to prevent collusion-based manipulation of trust scores.
10. The system (100) as claimed in claim 1, wherein the system further comprises smart contracts deployed within the blockchain to automate enforcement of trust rules, validation of trust scores, and initiation of countermeasures upon detection of malicious activities.