A Novel Two Step Computer Network Attack and Defense Strategy
Qiaohua Bai · 2024
The rapid evolution of cyber-physical systems has created a landscape of increased interconnectivity, underscoring the importance of securing networked infrastructures against diverse cyber threats. This study presents a novel two-step computer network attack and defense strategy tailored to strengthen network security and effectively mitigate cyber risks. By integrating reinforcement learning-based methods and penetration testing techniques, the strategy provides a comprehensive approach to improving network resilience and thwarting malicious attacks. Reinforcement learning-based methods bring adaptability and intelligence to the defense framework, enabling dynamic responses to evolving threats. By learning from past interactions, the system optimizes defensive strategies and proactively anticipates adversary maneuvers, ensuring that network defenses stay ahead of emerging threats. Complementary penetration testing techniques systematically evaluate network defenses, identify vulnerabilities, and enable proactive remediation. By analyzing network configurations and vulnerabilities, targeted defense strategies are developed to mitigate specific cyber risks. Through extensive experimentation, the effectiveness of the strategy is rigorously evaluated, demonstrating superior vulnerability detection accuracy compared to existing methods. The results confirm its effectiveness, with an average detection accuracy of 95.2%, underscoring its practicality for strengthening network security in the face of evolving threats.