Mitigating malware prevalence in networks with arbitrary topologies: a Flip-It cyber game approach integrated with epidemic modeling

Abstract

Cyber threats have evolved in complexity, aiming at a wide range of sectors using advanced methods and tools. This evolving threat landscape challenges existing cybersecurity frameworks, many of which lack the adaptability to counteract the complex tactics of sophisticated adversaries. Developing robust cyber defense strategies requires simulating dynamic interactions between attackers and defenders across high, moderate, and low-impact scenarios. The Flip-It cyber game serves as an intelligent framework for simulating these interactions, enabling the analysis of adaptive strategies in cybersecurity. This paper aims to address the problem of mitigating malware prevalence with full consideration of attack/defense capabilities in arbitrary network topologies. This paper proposes a sophisticated discrete-time epidemic model to characterize security state transitions over time for all three scenarios within the Flip-It game framework. On this basis, the original problem is modeled as a closed-loop control problem to seek the optimal containment strategy. Deep Reinforcement Learning (DRL) is then used to tackle the problem, generating efficient defense strategies that are well-adapted to changing cybersecurity environments.

Numerical simulations based on small-world networks, scale-free networks, and router networks are then carried out to generate corresponding strategies. Additionally, we have evaluated the performance of the proposed method against the State-Of-The-Art (SOTA) in terms of attack/defense objective function, control actions, number of devices under the control of the attacker and defender, stability, execution time, and scalability. This comprehensive approach integrates epidemiological modeling, game theory, and advanced machine learning to effectively tackle the complexities of contemporary cybersecurity threats.