Verifiable Random Function (VRF) Jury and Swarm Secured Routing: A Nature Inspired Cyber Security Framework for Decision Support Systems

Abstract

This paper presents a novel cybersecurity framework for decision support systems, inspired by ecological dynamics and cryptographic randomness. The proposed architecture replaces static routing and centralized security with three tightly integrated modules: a Verifiable Random Function (VRF) Jury Selection Engine, a Distributed Predator-Prey Anomaly Detection Network, and a Reinforcement Learning Adaptive Routing Controller. The core motivation is to eliminate fixed trust anchors and enable adaptive, verifiable oversight without a single point of failure. At each epoch, the VRF engine cryptographically selects a small jury of nodes from the active network, using a public-key-based pseudorandom function and a hash-based ordering. This selection is unpredictable, unbiased, and publicly verifiable by any participant. The jury then executes a bio-inspired anomaly detection algorithm modeled on Lotka-Volterra predator-prey dynamics. Each jury member maintains predator and prey populations for each traffic flow, updated based on packet features extracted by a lightweight one-class support vector machine. Packets are flagged when predator populations exceed a threshold, and a weighted majority vote among jury members produces the final anomaly verdict. This verdict, combined with real-time topology and latency data, feeds into a deep Q-network routing controller. The controller selects optimal next-hop paths while dynamically avoiding nodes with high anomaly scores, using a reward function that penalizes latency, threat proximity, and hop count. The entire system forms a closed loop: the jury oversees every packet, the ecological detector validates threats, and the reinforcement learner reroutes traffic accordingly. We demonstrate that this framework provides strong security guarantees through cryptographic randomness and ecological adaptation, while maintaining scalability through modular integration with existing protocol handlers. The primary contributions are a verifiable, non-deterministic jury selection mechanism, a nature-inspired anomaly detector with decentralized consensus, and a reinforcement learning routing module that jointly optimizes for security and performance.