Defense Algorithm

Defense algorithms aim to protect systems and models from malicious attacks, encompassing diverse areas like cybersecurity, machine learning, and robotics. Current research focuses on developing robust and efficient defense mechanisms using techniques such as causal modeling, deep learning (including multi-node representation learning and adversarial training), and game theory (including Stackelberg games and optimal stopping games), often applied within specific model architectures like autoencoders or reinforcement learning frameworks. These advancements are crucial for enhancing the security and reliability of various systems, from critical infrastructure to AI applications, by mitigating the impact of adversarial attacks and improving overall system resilience.