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.

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