IOT CYBER ATTACK DETECTION USING MACHINE LEARNING

Abstract

The rapid expansion of the Internet of Things has transformed modern digital ecosystems by enabling seamless connectivity among billions of smart devices across healthcare, transportation, industrial automation, smart homes, and critical infrastructure. While IoT technology improves efficiency and automation, it also introduces significant cybersecurity challenges due to constrained device resources, heterogeneous communication protocols, and decentralized architectures. Traditional security mechanisms struggle to cope with the evolving scale and sophistication of cyber attacks targeting IoT environments, including denial-of-service attacks, botnet intrusions, data injection, spoofing, and unauthorized access. In this context, machine learning has emerged as a powerful approach for intelligent cyber attack detection by learning patterns from large volumes of network traffic data and identifying anomalous behavior in real time. This project focuses on designing an IoT cyber attack detection system using machine learning techniques, with particular emphasis on Support Vector Machine classification. The proposed approach leverages data preprocessing, feature extraction, and supervised learning to distinguish normal traffic from malicious activities effectively. By automating threat detection and improving accuracy, the system aims to enhance the security and reliability of IoT networks while reducing manual intervention. The outcome of this work contributes toward developing scalable, adaptive, and intelligent intrusion detection mechanisms suitable for dynamic IoT ecosystems.