Attack Detection

Attack detection research focuses on identifying malicious activities in diverse systems, from IoT networks and autonomous vehicles to blockchain platforms and deep learning models. Current efforts leverage advanced machine learning techniques, including deep neural networks (like transformers and autoencoders), ensemble methods, and explainable AI (XAI) to improve accuracy, interpretability, and generalizability across various attack types and data modalities. These advancements are crucial for enhancing cybersecurity, ensuring the reliability of critical infrastructure, and mitigating the growing threat of sophisticated adversarial attacks in various domains.