Reinforcement Learning for Adaptive Cyber Defense: A Dynamic Approach to Threat Mitigation
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Abstract
In the rapidly evolving landscape of cyber threats, static defense mechanisms are often insufficient. This paper introduces a reinforcement learning-based framework for adaptive cyber defense that dynamically adjusts security measures based on the evolving threat landscape. By modeling cybersecurity as a decision-making process, the framework learns from past incidents and continuously optimizes responses to emerging threats. Experimental results demonstrate that this adaptive approach significantly improves the efficacy of threat mitigation strategies, reducing the impact of cyberattacks and enhancing overall security posture.
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