Active Defense

Active defense in cybersecurity and machine learning focuses on proactively mitigating threats rather than simply reacting to attacks. Current research emphasizes developing AI-driven systems, including multi-agent reinforcement learning and deep learning models, to anticipate and counter adversarial actions such as model extraction, evasion attacks, and data poisoning. These techniques aim to enhance the robustness and resilience of systems against increasingly sophisticated attacks, with applications ranging from securing smart grids to protecting machine learning models deployed as services. The field is also increasingly concerned with the fairness and ethical implications of these active defenses, ensuring they do not disproportionately impact certain user groups.