Deep reinforcement learning for cybersecurity

Document Type

Book Chapter

Source of Publication

Explainable Artificial Intelligence Xai for Next Generation Cybersecurity Concepts Challenges and Applications

Publication Date

10-15-2025

Abstract

The use of deep reinforcement learning (DRL) techniques in cybersecurity examined, with an emphasis on the importance of DRL in combating the growing complexity of cyber threats. The idea of DRL, how important it is to adaptive defense mechanisms. The basics of DRL, including deep neural networks and reinforcement learning. DRL helps autonomous agents interact with their surroundings and develop the best defense tactics. The uses of DRL in cybersecurity are then covered in detail, including vulnerability evaluation, phishing detection, malware analysis, and intrusion detection. Case studies and real-world examples show how DRL may improve the detection, analysis, and response capacities across a range of security disciplines. DRL in cybersecurity faces several difficulties despite its revolutionary promise, including scalability, interpretability, and adversarial assaults. Properly handle these issues, examine these obstacles and talk about new developments and potential research areas, including federated learning, multi-agent systems, and privacy-preserving methods. Finally, we demonstrate effective case studies and real-world applications of DRL-based cybersecurity solutions, emphasizing the role of defense mechanisms that are adaptable play in thwarting new attacks. However, there is the need for more study and cooperation fully utilize DRL for cybersecurity applications, as well as how DRL can completely transform cybersecurity defenses against ever-changing threats.

ISBN

[9781837240319, 9781837240326]

Publisher

The Institution of Engineering and Technology

First Page

111

Last Page

142

Disciplines

Computer Sciences

Keywords

Network Security and Intrusion Detection, Advanced Malware Detection Techniques, Smart Grid Security and Resilience

Scopus ID

105026740002

Indexed in Scopus

yes

Open Access

no

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