Nash Q-Network for Multi-Agent Cybersecurity Simulation

2025-08-30

AI Summary

To address the challenges of complex policy training, environmental non-stationarity, and difficulty in equilibrium convergence in multi-agent cybersecurity adversarial settings, this paper proposes a Nash-equilibrium-based multi-agent reinforcement learning framework. Under a zero-sum Markov game formulation, we design a Nash-Q Network to enable synchronous optimization and stable convergence of attacker and defender policies. The framework integrates the robust policy update mechanism of Proximal Policy Optimization (PPO), the value estimation capability of Deep Q-Networks (DQN), and the equilibrium-solving properties of Nash-Q learning, augmented by distributed data collection and a customized neural network architecture. Experimental results in a complex network defense simulation environment demonstrate that the method efficiently learns Nash-optimal policies, significantly improving defensive robustness and training stability. These findings validate the framework’s effectiveness, convergence guarantee, and practical applicability in non-stationary multi-agent adversarial scenarios.

Technology Category

• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

Abstract

Cybersecurity defense involves interactions between adversarial parties (namely defenders and hackers), making multi-agent reinforcement learning (MARL) an ideal approach for modeling and learning strategies for these scenarios. This paper addresses one of the key challenges to MARL, the complexity of simultaneous training of agents in nontrivial environments, and presents a novel policy-based Nash Q-learning to directly converge onto a steady equilibrium. We demonstrate the successful implementation of this algorithm in a notable complex cyber defense simulation treated as a two-player zero-sum Markov game setting. We propose the Nash Q-Network, which aims to learn Nash-optimal strategies that translate to robust defenses in cybersecurity settings. Our approach incorporates aspects of proximal policy optimization (PPO), deep Q-network (DQN), and the Nash-Q algorithm, addressing common challenges like non-stationarity and instability in multi-agent learning. The training process employs distributed data collection and carefully designed neural architectures for both agents and critics.

Problem

• Modeling multi-agent adversarial interactions in cybersecurity defense scenarios
• Addressing training complexity and non-stationarity in multi-agent reinforcement learning
• Developing Nash-optimal strategies for robust cybersecurity defense systems

Innovation

• Nash Q-Network for multi-agent cybersecurity
• Combines PPO, DQN and Nash-Q algorithms
• Distributed data collection with neural architectures