Gen-Power: Anomaly Detection in IoT Devices Utilizing Generated Power Waveforms

Recently, IoT (Internet-of-Things) devices are very widely used in consumer electronics areas and their design and manufacturing are often outsourced to third parties to make them at a low cost. Meanwhile, malfunctions may be inserted into them intentionally by malicious third parties. Utilizing power waveforms measured from IoT devices is one of the effective ways to detect its anomalous behaviors. Most IoT devices regularly consume steady-state power due to the operating system and/or hardware components. However, the existing methods manually or semi-manually find out the steady-state power in the IoT device and extract pre-determined features from the application power waveform. Hence, they cannot well detect its anomalous behaviors automatically. In this paper, we propose a method, called Gen-Power, to detect anomalous behaviors in IoT devices utilizing the generative machine-learning model. The proposed method generates application power waveforms by inferring the steady-state power by machine learning from the observed total power waveform. Then, the anomalous application behaviors are detected by automatically extracting the latent features from the generative application power waveform. Experimental evaluations show that Gen-Power detects anomalous application behaviors successfully, while the recent state-of-the-art method cannot detect them.