Reinforcement Learning for Cyber Security: Methods and Applications
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Security and Privacy".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 96
Special Issue Editors
2. Cybersecurity Institute, University of Liverpool, Liverpool, UK
Interests: cyber security; digital forensics; Internet of Things; reinforcement learning; large language models; forensics; risk management and governance
Interests: reinforcement learning; formal methods for security; authorisation and accountability; artificial intelligence for security, verification, and embedded systems
Special Issue Information
Dear Colleagues,
Modern cybersecurity faces escalating threats from AI-driven attacks, zero-day exploits, and complex attack surfaces. Traditional rule-based systems struggle with adaptability, while reliance on human experts is costly and unscalable. Reinforcement learning (RL) has emerged as a transformative alternative, enabling autonomous and adaptive decision-making through exploratory learning—simulating attack–defend dynamics to uncover novel strategies. Its ability to iteratively optimize actions in uncertain environments makes RL ideal for proactive defence, reducing dependency on manual intervention. Applications span cyber investigation and forensics (e.g., automated log analysis, attack traceability), cybersecurity by design (embedding RL into system architectures for real-time resilience), and risk/vulnerability assessment (predictive modelling of threat landscapes). This Special Issue invites research advancing RL-driven solutions for modern challenges.
Topics Include:
- RL for threat detection, penetration testing, and incident response;
- Autonomous cyber forensics and adversarial traceability;
- Cybersecurity-by-design frameworks with embedded RL;
- Risk assessment, vulnerability prioritization, and compliance automation;
- RL in Industrial Internet of Things (IIoT) security and critical infrastructure protection;
- Exploratory RL for simulating advanced persistent threats (APTs);
- Privacy-aware RL in threat intelligence sharing;
- Scalable RL for zero-day exploit detection;
- Cyber incident response.
We are seeking theoretical breakthroughs, real-world case studies, and interdisciplinary approaches that integrate RL with game theory, digital twins, or explainable AI. Studies addressing ethical AI use, robustness against adversarial RL, and regulatory alignment (e.g., NIST, GDPR) are encouraged.
Dr. Mohamed Chahine Ghanem
Prof. Dr. Dominik Wojtczak
Prof. Dr. Vassil Vassilev
Guest Editors
Manuscript Submission Information
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Keywords
- reinforcement learning
- cyber security
- autonomous defence
- Markov chain
- cyber forensics
- threat detection
- vulnerability assessment
- Internet of Things security
- compliance automation
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