  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 identify this intrusion
behavior on IoT devices but still lack accurate models in real-time for
ubiquitous botnet detection.
  We proposed the first online intrusion detection system called DeepAuditor
for IoT devices via power auditing. To develop the real-time system, we
proposed a lightweight power auditing device called Power Auditor. We also
designed a distributed CNN classifier for online inference in a laboratory
setting. In order to protect data leakage and reduce networking redundancy, we
then 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, and the proposed classifier
outperformed a baseline classifier, especially against unseen patterns. 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.
