TRUST-SDN: A Machine Learning-driven Trust Scoring Framework for Dependability-aware Routing in Mobile Ad-hoc Networks
Keywords:
Compliance-aware routing, Cyber-physical reliability, Machine learning, Network dependability, Software-defined networking, Trust scoringAbstract
Software-Defined Networking (SDN) decouples the control and data planes to enable programmable, centrally managed network behaviour, yet this same centralization concentrates dependability and compliance risk within a single control point. Existing dependability mechanisms for SDN deployments largely rely on static thresholds or binary intrusion-detection outputs that fail to capture the graded, time-machine learning-driven trust scoring framework that fuses behavioural telemetry, control-plane responsiveness, and compliance-adherence signals into a continuous, exponentially weighted trust score that drives dependability-aware and compliance-sensitive routing decisions. The framework comprises four cooperating modules: a Behavioural Telemetry Engine, a Composite Trust Aggregator, a Compliance-Aware Routing Engine, and a Dependability Regression Monitor. A formal boundedness theorem establishes that the proposed trust update rule remains stable within a closed interval under adversarial perturbation. Simulation on Mininet-based fat-tree topologies under varying adversarial loads demonstrates that TRUST-SDN improves detection accuracy, reduces mean time to recovery, and sustains markedly higher compliance-violation detection rates than static-threshold and Bayesian trust baselines, confirming its suitability for hyperconnected, compliance-sensitive future internet deployments.
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