DeepAuditor: Distributed Online Intrusion Detection System for IoT devices via Power Side-channel Auditing

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 information to characterize this intrusion behavior on IoT devices but still lack real-time detection approaches. This study aimed to design an online intrusion detection system called DeepAuditor for IoT devices via power auditing. To realize the real-time system, we first proposed a lightweight power auditing device called Power Auditor. With the Power Auditor, we developed a Distributed CNN classifier for online inference in our laboratory setting. In order to protect data leakage and reduce networking redundancy, we also proposed a privacy-preserved inference protocol via Packed Homomorphic Encryption and a sliding window protocol in our system. The classification accuracy and processing time were measured in our laboratory settings. We also demonstrated that the distributed CNN design is secure against any distributed components. Overall, the measurements were shown to the feasibility of our real-time distributed system for intrusion detection on IoT devices.