Article ID: 2024EAP1145
Recently, IoT (Internet-of-Things) devices are very widely used in our daily lives 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 and we have to remove it from the total power to detect anomalous behaviors. However, the existing methods manually or semi-manually remove the steady-state power and further they utilize the pre-determined features in the power waveform to detect anomalies. Hence, they cannot well detect them automatically. In this paper, we propose a method, called Gen-Power2, to detect anomalous behaviors in IoT devices utilizing the generative machine-learning model. The proposed method generates an application power waveform 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 generated application power waveform. Experimental evaluations show that Gen-Power2 detects anomalous application behaviors successfully, while the recent state-of-the-art method cannot detect them.
