DQN and PPO policies, trained with a graph-aware action mask called an action-displacement adjacency matrix, beat random and greedy baselines on multi-step Colonel Blotto games on small graphs.
An Efficient Security Mechanism for High-Integrity Wireless Sensor Networks
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abstract
Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Unfortunately, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes and intentional compromise of nodes by an insider attack in a WSN pose particularly difficult challenges to security engineers as these attacks cannot be defended by traditional cryptography-based mechanisms. In this paper, a security solution is proposed for detecting compromised and faulty nodes in a WSN. The mechanism also isolates a compromised node from the network so that it cannot participate in any network activity. The proposed mechanism is based on misbehavior classification, behaviour monitoring and trust management. It involves minimum computation and communication overhead and is ideally suited for a resource-constrained, high-integrity WSN.
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Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs
DQN and PPO policies, trained with a graph-aware action mask called an action-displacement adjacency matrix, beat random and greedy baselines on multi-step Colonel Blotto games on small graphs.