Machine Learning based Security for IoT Networks

Abstract

The sheer scale and diversity of IoT networks, coupled with limited computational resources and heterogeneous environments, make traditional security approaches ineffective in safeguarding IoT systems. Cybersecurity in IoT networks is complicated by the dynamic and resource-constrained nature of IoT devices, making them more vulnerable to a wide array of cyber-attacks, including unauthorized access, data breaches, and denial-of-service (DoS) attacks. Therefore, the need for robust and efficient intrusion detection systems (IDS) tailored for IoT environments is more critical than ever.