DEVELOPING AN ADAPTIVE AND PROACTIVE CYBERSECURITY DEFENSE MECHANISM (APCDM) USING DEEP REINFORCEMENT LEARNING IN WSN

Abstract

In the domain of Wireless Sensor Networks (WSNs), where resources are limited and dynamic threats persist, there's a growing requirement for cybersecurity defenses that can adapt and act ahead of time. This research introduces an innovative strategy to tackle this challenge by harnessing the capabilities of Deep Reinforcement Learning (DRL). The study proposes an original method for an Adaptive and Proactive Cyber security Defense Mechanism (APCDM) utilizing Deep Reinforcement Learning within WSNs. The approach entails constructing a simulated cybersecurity environment that accurately imitates real-world threats and network behaviors, allowing for the training and assessment of a DRL agent. This agent engages with the environment, acquiring optimal defensive strategies through advanced DRL algorithms such as Advantage Actor-Critic (A2C) or Trust Region Policy Optimization (TRPO). The agent's goal is steered by a meticulously crafted reward system that encourages actions minimizing vulnerabilities and effectively countering attacks. The resulting mechanism represents a notable advancement in WSN cybersecurity, providing an automated, adaptable, and forward-looking approach to protecting these networks against an ever-changing landscape of threats.