IoT Intrusion Detection

Internet of Things (IoT) intrusion detection focuses on developing robust methods to identify and prevent cyberattacks targeting the growing number of interconnected devices. Current research heavily utilizes machine learning, particularly focusing on ensemble methods like XGBoost and LightGBM, deep learning architectures like autoencoders, and transfer learning techniques to address data scarcity and improve model generalizability. These advancements aim to enhance the security and reliability of IoT networks, impacting both the scientific understanding of cybersecurity threats and the practical implementation of effective defense mechanisms.