Reinforcement Learning for Adaptive Cybersecurity Policy Optimization

EasyChair Preprint 14012

Abstract

This research paper explores the application of reinforcement learning techniques for the optimization of cybersecurity policies. With the increasing complexity and sophistication of cyber threats, traditional rule-based approaches fall short in effectively adapting to evolving attack strategies. Reinforcement learning offers a promising solution by allowing an autonomous agent to learn optimal cybersecurity policies through trial and error interactions with its environment.

The study begins by outlining the challenges faced by traditional cybersecurity approaches and the need for adaptive policies. It then introduces reinforcement learning as a powerful approach for policy optimization. The paper discusses the key components of reinforcement learning, including the agent, environment, actions, rewards, and learning algorithm.

Furthermore, the research presents a detailed analysis of existing reinforcement learning methods for cybersecurity policy optimization, highlighting their strengths and limitations. It also explores the use of deep reinforcement learning techniques, such as deep Q-learning and policy gradient methods, in tackling the complexity of cybersecurity environments.

Keyphrases: Cybersecurity, algorithm, learning

@booklet{EasyChair:14012,
  author    = {Kaledio Potter and Dylan Stilinki and Selorm Adablanu},
  title     = {Reinforcement Learning for Adaptive Cybersecurity Policy Optimization},
  howpublished = {EasyChair Preprint 14012},
  year      = {EasyChair, 2024}}