SMART CYBERSECURITY STRATEGIES: MACHINE LEARNING FOR IOT ATTACK IDENTIFICATION AND PREVENTION

Abstract

The pervasive integration of Internet of Things (IoT) devices in today's networked environment has introduced several conveniences and opportunities. This technological revolution has also introduced a new category of cyber risks, as attackers use weaknesses in IoT devices to undermine user privacy, disrupt essential services, and cause chaos. Conventional security methods have demonstrated insufficiency in addressing the increasing complexity of cyber-attacks, requiring a more sophisticated and adaptable strategy. This urgency has prompted the creation of a Machine Learning Model for Cyber Attack Detection and Classification in IoT Environments (ML-IoT-CD). The necessity for a solid cybersecurity solution in IoT environments has grown essential due to the growing dependence on these devices for vital applications. Current intrusion detection systems and traditional security measures frequently lack the scalability and agility required to adapt to swiftly advancing attack methodologies. Consequently, there is an urgent need for an intelligent, automated, and proactive cyber security system that can identify and classify developing cyber threats in real time. The ML-IoT-CD paradigm seeks to address this requirement by utilizing machine learning techniques to examine extensive data produced by IoT devices. This approach may efficiently differentiate between legal and harmful activity, therefore enhancing the security posture of IoT networks.