DeepAuditor: Distributed Online Intrusion Detection System for IoT Devices via Power Side-channel Auditing
Woosub Jung, Yizhou Feng, Sabbir A. Khan, Chunsheng Xin, Danella Zhao, Gang Zhou · 2022
As the number of IoT devices has increased rapidly, IoT botnets have exploited the vulnerabilities of IoT devices. However, it is still challenging to detect the initial intrusion on IoT devices prior to massive attacks. Recent studies have utilized power side-channel in-formation to identify this intrusion behavior on IoT devices but still lack accurate models in real-time for ubiquitous botnet detection. We propose the first online intrusion detection system called DeepAuditor for multiple IoT devices via power auditing. To de-velop the real-time system, we propose a lightweight power auditing device called Power Auditor. We also design a distributed CNN classifier for online inference in a laboratory setting. In order to protect data leakage and reduce networking redundancy, we then propose a privacy-preserved inference protocol via Packed Homo-morphic Encryption and a sliding window protocol in our system. The classification accuracy and processing time are measured, and the proposed classifier outperforms a baseline classifier, especially against unseen patterns. We also demonstrate that the distributed CNN design is secure against any distributed components. Over-all, the measurements are shown to the feasibility of our real-time distributed system for intrusion detection on IoT devices.