Empowering IoT Cyber Network Attacks Using Machine Learning

This paper investigates the dual role of machine learning as a tool for enhancing and undermining IoT cybersecurity. Machine Learning systems can process immense volumes of IoT Data, identifying patterns in traffic, flagging anomalies indicative of cyber threats and forecasting vulnerabilities. These features enable IoT networks to automatically counter and blunt the effects of impending attacks. Nevertheless, such methods can also be used by malicious users to design increasingly sophisticated cyberattacks. Attackers may utilize Machine Learning to discover, potentially circumventing traditional defenses. Hence, while leveraging machine learning for IoT security, it is crucial to address its potential for misuse.