A Review of Techniques and Policies on Cybersecurity Using Artificial Intelligence and Reinforcement Learning Algorithms

Abstract

Cybersecurity is a critical process that safeguards networks, systems, and applications against cyber-attacks, wherein digital information is targeted for unauthorized access, manipulation, or destruction. As attackers continually evolve their tactics, addressing cybersecurity challenges has become paramount, especially in sensitive domains like the military and defense industries. This article delves into the challenges that artificial intelligence (AI) faces in the military domain, specifically focusing on defense applications. We review AI algorithms relevant to defense, examining their potential applications and benefits: much of this study revolves around cybersecurity in defense applications, particularly within cyber-physical systems (CPS). We explore reinforcement learning (RL) and deep RL (DRL) algorithms in CPS, aiming to enhance understanding of the cybersecurity implications in this domain. In this context, we present RL and DRL algorithms employed in cyber-attacks and their potential threats and vulnerabilities. Furthermore, we discuss how RL and DRL algorithms can be effectively leveraged for cyber-attack detection and defense applications, providing usable insights into bolstering CPS cybersecurity. By addressing both technical aspects and ethical considerations, this article offers a comprehensive view of the challenges and opportunities surrounding cybersecurity in defense applications.