ADRIoT: An Edge-Assisted Anomaly Detection Framework Against IoT-Based Network Attacks
Ruoyu Li, Qing Li, Jianer Zhou, Yong Jiang
Abstract
Internet of Things (IoT) has entered a stage of rapid development and increasing deployment. Meanwhile, these low-power devices typically cannot support complex security mechanisms and, thus, are highly susceptible to malware. This article proposes ADRIoT, an anomaly detection framework for IoT networks, which leverages edge computing to uncover potential threats. An edge is empowered with an anomaly detection module, which consists of a traffic capturer, a traffic preprocessor, and a collection of anomaly detectors dedicated to each type of device. Each detector is constructed by an LSTM autoencoder in an unsupervised manner that requires no labeled attack data and is able to handle emerging zero-day attacks. When a device connects to the edge, the edge will fetch the corresponding detector from the cloud and execute it locally. Another problem is the resource constraint of a single edge device like a home router hinders the deployment of such a detection module. To mitigate this problem, we design a multiedge collaborative mechanism that integrates the resource of multiple edges in a local network to increase the overall load capacity. The evaluation demonstrates that ADRIoT can detect various IoT-based attacks effectively and efficiently, showing that ADRIoT can feasibly help build a more secure IoT environment.