HADS: Hybrid Anomaly Detection System for IoT Environments

Abstract

IoT (Internet of Things) devices are rapidly becoming popular in residential environments, but security is still a big concern in this ecosystem. The fast growth of IoT devices in homes and new attacks targeting these devices require a smart detection solution to protect this heterogeneous environment. In this paper, we present an attack detection approach based on machine learning techniques for anomaly detection, and a decision module, with the goal of identifying relevant attacks on IoT network. The approach is implemented on a single-board computer and systematically evaluated using various protocol attacks and commercial off-the-shelf IoT devices to verify its effectiveness and feasibility in a realistic scenario. The results obtained in the experimental evaluation indicate that our proposed approach can be applied to protect IoT devices against the considered attacks with accuracy of 94%-99% and detection time less than 0.7s.