Machine learning-based early detection of IoT botnets using network-edge traffic

Abstract

In this work, we present an IoT botnet detection solution, EDIMA, consisting of a set of lightweight modules designed to be deployed at the edge gateway installed in home networks with the remaining modules expected to be implemented on cloud servers. EDIMA targets early detection of IoT botnets prior to the launch of an attack and includes a novel two-stage Machine Learning (ML)-based detector developed specifically for IoT bot detection at the edge gateway. The ML-based bot detector first employs supervised ML algorithms for aggregate traffic classification and subsequently Autocorrelation Function (ACF)-based tests to detect individual bots. The EDIMA architecture also comprises a malware traffic database, a policy engine, a feature extractor and a traffic parser. Performance evaluation results using our testbed setup with real-world IoT malware traffic as well as other public IoT datasets show that EDIMA achieves high bot scanning and bot-CnC traffic detection accuracies with very low false positive rates. The detection performance is also shown to be robust to an increase in the number of IoT devices connected to the edge gateway where EDIMA is deployed. Further, the runtime performance analysis of a Python implementation of EDIMA deployed on a Raspberry Pi reveals low bot detection delays and low RAM consumption. EDIMA is also shown to outperform existing detection techniques for bot scanning traffic and bot-CnC server communication.