Talk: Securing Networks with Reinforcement Learning & Game Theory

Thu, 23 Jan 2025 10:32:43 -0500
agent, ai, cybersecurity, game-theory, machine-learning, multiagent, reinforcement-learning

Securing Distributed Networks: Leveraging Reinforcement Learning and Game Theory for Attack Detection and Mitigation

Dr. Md Tariqul Islam, Syracuse University

10-11am January 30, 2025; ITE 459, UMBC and online

Reinforcement learning (RL) has demonstrated remarkable success across diverse domains, from mastering complex games to optimizing real-time feedback systems in robotics and industrial control. However, its potential in cybersecurity, particularly for autonomous attack detection and mitigation in distributed systems, remains largely underexplored. Traditional single-agent RL approaches struggle in decentralized environments where multiple entities make independent decisions, necessitating multi-agent reinforcement learning (MARL). Our research explores blockchain networks as an ideal test case due to their decentralized architecture and trustless consensus mechanisms. We developed a novel MARL-based consensus mechanism for Proof-of-Stake blockchains, enabling nodes to collaboratively identify and penalize malicious behavior while preserving decentralization. This approach effectively mitigated six major blockchain attack types with minimal computational overhead. Building on these results, we propose integrating game-theoretic principles into the MARL framework to model adversarial strategies and enhance system resilience. The synergy between reinforcement learning and game theory establishes a robust foundation for dynamic and adaptive security in distributed systems, effectively addressing current vulnerabilities while anticipating and countering future threats. This integrated approach enables the design of resilient, scalable defense mechanisms tailored to the complex dynamics of decentralized architectures.

Dr. Md Tariqul Islam is an Assistant Professor of Trustworthy Cyberspace in the School of Information Studies (iSchool) at Syracuse University. His research focuses on advancing the security, efficiency, and fault tolerance of networks and distributed systems, particularly in the domains of cloud and blockchain technologies.

UMBC Center for AI