Adaptive Defense

Adaptive defense mechanisms in machine learning aim to create robust systems that can withstand adversarial attacks, which are increasingly sophisticated and tailored to specific defenses. Current research focuses on developing adaptive algorithms, such as reinforcement learning and learning automata, to dynamically adjust defenses based on observed attacks, often leveraging techniques like distribution learning and adversarial game theory to optimize both attack and defense strategies. This field is crucial for securing machine learning systems in real-world applications, ranging from federated learning and face recognition to cryptocurrency and IoT security, where vulnerabilities can have significant consequences.