Machine Learning for Cybersecurity in IoT Networks: Detecting and Preventing Threats

Abstract

As IoT networks expand, cybersecurity threats are becoming more sophisticated. Machine learning offers an advanced approach to threat detection, anomaly recognition, and real-time intrusion prevention. This paper explores ML techniques such as supervised learning, reinforcement learning, and adversarial networks in cybersecurity applications. It discusses challenges like data privacy, adversarial attacks, and model training efficiency. Case studies highlight real-world applications of ML for IoT security, showcasing its effectiveness in mitigating cyber threats and securing connected devices.