Communication Graph Vulnerability to Malicious Agents

TITLE OF THE INVENTION

Hierarchical Adaptive Defense Framework for Multi‑Agent System Communication Graphs

FIELD OF THE INVENTION

The present invention relates to distributed multi‑agent systems (MAS), specifically to resilient consensus protocols and adaptive graph reconfiguration techniques that safeguard communication graphs against malicious actors.

BACKGROUND AND PRIOR ART

Existing consensus mechanisms such as the Weighted Mean‑Subsequence‑Reduced (W‑MSR) algorithm rely on global knowledge of network robustness and a fixed bound on malicious neighbors ^[1][2][3]. When communication links are compromised—through packet loss, delay, or intentional tampering—the assumptions underlying W‑MSR are violated, leading to failure of consensus ^[4]. Moreover, the combinatorial nature of (r, s)‑robustness renders global enforcement impractical for large‑scale MAS deployments ^[1][2]. Prior work has attempted to embed trusted agents forming a connected dominating set (CDS) to localize misinformation ^[v12699], but this approach still requires global coordination. Adaptive algorithms that increase connectivity only when necessary have been proposed to balance robustness and vulnerability ^[v12472], yet they lack formal guarantees in the presence of dynamic link attacks. Recent studies demonstrate that deliberate manipulation of graph structure—through targeted edge perturbations or curvature‑based metrics—can attenuate attack propagation ^{[v13048][v15436]}. These limitations motivate the need for a locally enforceable, adaptive defense architecture.

SUMMARY OF THE INVENTION

The invention discloses a hierarchical, adaptive defense framework that integrates Local Robustness Certification (LRC), Secure Graph‑Aware Consensus (SGC), Cascading Attack Mitigation Layer (CAML), and Resilience‑Oriented Graph Evolution (ROGE). LRC enables each agent to compute a lightweight robustness score from its immediate neighborhood and exchange concise certificates, allowing local reconfiguration when the score falls below a threshold. SGC replaces W‑MSR with a trust‑weighted consensus that incorporates zero‑trust signed MQTT messages and dynamic influence radii derived from graph‑adaptive filtering. CAML detects anomalous propagation patterns and isolates suspect sub‑graphs via topology re‑segmentation and cryptographic sandboxes. ROGE models the communication graph as a dynamic graph and applies submodular optimization to autonomously add or remove edges, maximizing a global resilience objective while minimizing overhead. Together, these components provide scalable, formally guaranteed resilience against a wide spectrum of malicious behaviors.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Embodiment 1 – Local Robustness Certification (LRC)

Each agent i periodically computes a local robustness score R_i based on its degree d_i, clustering coefficient C_i, and recent message integrity checks. The score is encoded into a 2‑bit vector certificate v_i = [b_1, b_2] and broadcast to immediate neighbors. If R_i < τ (a predefined threshold), the agent initiates local reconfiguration: it adds edges to neighbors with high v_j or removes edges to neighbors with low v_j, thereby maintaining a minimum degree condition for resilient consensus ^[1][2]. The certificate exchange is lightweight and requires no global state.

Embodiment 2 – Secure Graph‑Aware Consensus (SGC)

SGC replaces the W‑MSR filtering rule with a weighted averaging step: each agent computes a trust score τ_j for neighbor j as a function of v_j and a cryptographic attestation (e.g., signed MQTT payload). The consensus update is x_i(t+1) = Σ_j τ_j x_j(t) / Σ_j τ_j. Zero‑trust identity verification is enforced by requiring each MQTT message to be signed with a device‑bound key stored in a TPM or secure element; the broker validates the signature before forwarding ^{[8][v7694][v5635]}. The influence radius is dynamically adjusted based on observed attack patterns, inspired by adaptive GNN filtering ^[9][v6049].

Embodiment 3 – Cascading Attack Mitigation Layer (CAML)

CAML monitors message propagation for anomalous bursts of identical payloads. Upon detection, it triggers a topology re‑segmentation that temporarily isolates suspect sub‑graphs, analogous to the centralized controller’s removal of malicious agents ^[10]. Each agent maintains a per‑agent Message Authentication Code (MAC) sandbox to contain potential code injection, following lessons from the SSH agent vulnerability ^[11] and secure IoT protocols ^[12].

Embodiment 4 – Resilience‑Oriented Graph Evolution (ROGE)

ROGE treats the communication graph as a dynamic graph G(t) = (V, E(t)). Edge reconfiguration actions are selected by a submodular optimization routine that maximizes a global resilience objective (e.g., Choquet‑integral based resilience metric) while minimizing communication overhead ^{[v6337][v4973][v7122][v5002]}. The greedy algorithm achieves a (1‑1/e) approximation for monotone submodular objectives under adversarial node removals ^[v5002]. Edge additions are limited to local neighborhoods to preserve scalability.

Embodiment 5 – Integration and Deployment

The framework is implemented on resource‑constrained edge devices. Certificates and trust scores are transmitted as 2‑bit vectors, and cryptographic operations use lightweight MACs or ECC signatures to satisfy embedded constraints ^[8][v15586]. The system can be deployed incrementally, integrating with existing MQTT brokers and leveraging zero‑trust identity verification.

CLAIMS

1. Method for maintaining resilient consensus in a multi‑agent system, comprising: (a) each agent computing a local robustness score from its immediate neighborhood; (b) exchanging a 2‑bit robustness certificate with immediate neighbors; (c) locally reconfiguring edges when the robustness score falls below a threshold; (d) computing a trust score for each neighbor based on received certificates and cryptographic attestations; (e) performing a weighted consensus update using the trust scores; (f) monitoring message propagation for anomalous bursts; (g) isolating suspect sub‑graphs and re‑segmenting the topology; and (h) selecting edge reconfiguration actions via submodular optimization to maximize a global resilience objective while minimizing communication overhead, wherein the submodular optimization is performed autonomously by each agent without global state knowledge.
2. System for resilient consensus in a multi‑agent system, comprising: a plurality of agents each having a local robustness certification module, a secure graph‑aware consensus module, a cascading attack mitigation module, and a resilience‑oriented graph evolution module, wherein the modules execute the steps of claim 1.
3. Method of claim 1, wherein the local robustness score is computed as a weighted sum of the agent’s degree, clustering coefficient, and a binary message integrity flag.
4. Method of claim 1, wherein the 2‑bit robustness certificate encodes a binary indicator of whether the agent’s degree exceeds a minimum threshold and a binary indicator of recent message integrity.
5. Method of claim 1, wherein the trust score for a neighbor is derived from the neighbor’s robustness certificate and a cryptographic attestation signed with a device‑bound key.
6. Method of claim 1, wherein the weighted consensus update is performed using a weighted mean‑subsequence‑reduced rule that discards the highest and lowest F values, where F is a locally determined bound on malicious neighbors.
7. Method of claim 1, wherein the cascading attack mitigation module detects anomalous bursts by comparing the variance of message payloads over a sliding window to a predefined threshold.
8. Method of claim 1, wherein the resilience‑oriented graph evolution module selects edge additions or removals by maximizing a Choquet‑integral based resilience metric subject to a submodular constraint.
9. Method of claim 1, wherein the submodular optimization routine is implemented as a greedy algorithm that iteratively selects the edge reconfiguration action yielding the largest marginal increase in the resilience metric.
10. Method of claim 1, wherein the system operates over an MQTT broker that requires each message to be signed with an ECC key and validated before forwarding.
11. Method of claim 1, wherein the agents maintain per‑agent MAC sandboxes to contain potential code injection.
12. Method of claim 1, wherein the local reconfiguration step adds edges to neighbors with robustness certificates above a high‑confidence threshold and removes edges to neighbors with certificates below a low‑confidence threshold.
13. Method of claim 1, wherein the trust score is updated at each consensus iteration based on the latest received certificates and a decay factor that reduces the influence of stale certificates.
14. Method of claim 1, wherein the weighted consensus update is performed in a fully distributed manner without any central coordinator.
15. Method of claim 1, wherein the resilience‑oriented graph evolution module operates asynchronously with the consensus module, allowing continuous adaptation to changing attack patterns.

ABSTRACT

A hierarchical adaptive defense framework for multi‑agent systems is disclosed. The framework integrates Local Robustness Certification (LRC), Secure Graph‑Aware Consensus (SGC), Cascading Attack Mitigation Layer (CAML), and Resilience‑Oriented Graph Evolution (ROGE). Each agent computes a lightweight robustness score from its immediate neighborhood, exchanges concise certificates, and locally reconfigures edges when the score falls below a threshold. Consensus is performed using trust‑weighted updates that incorporate zero‑trust signed MQTT messages and dynamically adjusted influence radii. Anomaly detection monitors message propagation for bursts, triggering isolation of suspect sub‑graphs and cryptographic sandboxes. Edge reconfiguration is guided by submodular optimization to maximize a global resilience objective while minimizing overhead. The system operates without global state knowledge, providing scalable, formally guaranteed resilience against a broad spectrum of malicious behaviors in distributed networks.