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The growing integration of Internet of Things (IoT) devices into various sectors like healthcare, transportation, and agriculture has dramatically increased their presence in everyday life. However, this rapid expansion has exposed new vulnerabilities within computer networks, creating security challenges. These IoT devices, often limited by their hardware constraints, lack advanced security features, making them easy targets for attackers and compromising overall network integrity. To counteract these security issues, Behavioral-based Intrusion Detection Systems (IDS) have been proposed as a potential solution for safeguarding IoT networks. While Behavioral-based IDS have demonstrated their ability to detect threats effectively, they encounter practical challenges due to their reliance on pre-labeled data and the heavy computational power they require, limiting their practical deployment. This research introduces the IoT-FIDS (Flow-based Intrusion Detection System for IoT), a lightweight and efficient anomaly detection framework tailored for IoT environments. Instead of employing traditional machine learning techniques, the IoT-FIDS focuses on identifying unusual behaviors by examining flow-based representations that capture standard device communication patterns, services used, and packet header details. By analyzing only benign traffic, this network-based IDS offers a streamlined and practical approach to securing IoT networks. Our experimental results reveal that the IoT-FIDS can accurately detect most abnormal traffic patterns with minimal false positives, making it a feasible security solution for real-world IoT implementations.
Ibrahim Mutambik (Wed,) studied this question.