RL-Based Adaptive Cyber Defence System

Abstract

In the current connected digital world, cyber defence can no longer function in the traditional and signature-based protection, but it should adopt machine-speed adaptivity and intelligence. The ruthless development of threats, which involves zero-day exploits and polymorphic malware, among others, makes the conventional security mechanisms all the more useless, making them slow to detect and leading to breaches. This chapter proposes a new and holistic approach to an adaptive cyber defence system with representatives of the Reinforcement Learning (RL) principles. We do not think of the process of defence as a complex of checks or a chain of checks, but rather as a decision process that is continuous or a sequence of decisions. The RL agent is integrated into the network setting and constantly monitors the traffic patterns, the precondition of the systems, and the actions of the possible attackers. The agent is given vital reward or penalty feedback based on these observations and the ensuing effect of its actions, i.e., firewall settings, honeypot implementation, or isolation of resources. Using such an iterative feedback process, the agent can learn an optimal dynamic defence policy. This policy allows the system to make smart distribution of scarce security assets, modify parameters in advance, and implement advanced countermeasures dynamically. The resulting defence capability becomes not an obstacle, which has some fixed, brittle point, but a living, evolving system that becomes increasingly smart and harder to defeat with each encounter against an adversary. The outlined strategy provides a structural change in the direction of genuinely independent and highly efficient cybersecurity, which resembles a human-like ability to learn and outsmart the enemy.