Application of the Learning Automaton Model for Ensuring Cyber Resiliency

Abstract

This work addresses the functional approach to ensuring cyber resiliency as a kind of adaptive security management. For this purpose, we propose a learning automaton model capable of self-learning and adapting to changes while interacting with the external environment. Each node in the under-controlled system has a set of probable actions with respect to neighboring nodes. The same actions are represented in the graph of the learning automaton, but the probabilities of actions in the graph model are permanently updated based on the received reinforcement signals. Due to the adaptive reconfiguration of the nodes, the system is able to counteract the cyberattacks, preserving resiliency. The experimental study results for the emulated wireless sensor network (WSN) are presented and discussed. The packets loss rate stays below 20% when the number of malicious nodes is 20% of the total number of nodes, while the common system loses more than 70% of packets. The network uptime with the proposed solution is 30% longer; the legitimate nodes detect malicious nodes and rebuild their interaction with them, thereby saving their energy. The proposed mechanism allows ensuring the security and functional sustainability of the protected system regardless of its complexity and mission.