An Anomalous Behavior Detection Method for IoT Devices Based on Power Waveform Shapes

Takasaki, Kazunari; Togawa, Nozomu

doi:10.1109/iolts56730.2022.9897477

Cited by 2 publications

(1 citation statement)

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“…For example, using an FPGA device, we implemented an AES encryption circuit and a hardware Trojan that leaked the encryption key, as a preliminary experiment. In this case, the proposed method successfully detected the anomalous behavior caused by the hardware Trojan [22]. However, a hardware Trojan can be implemented using very small logic circuits, and its power consumption can become too small.…”

Section: Effect Of Invariances On Detection Results (Application 2)mentioning

confidence: 96%

An Anomalous Behavior Detection Method Utilizing IoT Power Waveform Shapes

Takasaki

Togawa

2024

IEICE Trans. Fundamentals

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In recent years, with the wide spread of the Internet of Things (IoT) devices, security issues for hardware devices have been increasing, where detecting their anomalous behaviors becomes quite important. One of the effective methods for detecting anomalous behaviors of IoT devices is to utilize consumed energy and operation duration time extracted from their power waveforms. However, the existing methods do not consider the shape of time-series data and cannot distinguish between power waveforms with similar consumed energy and duration time but different shapes. In this paper, we propose a method for detecting anomalous behaviors based on the shape of time-series data by incorporating a shapebased distance (SBD) measure. The proposed method first obtains the entire power waveform of the target IoT device and extracts several application power waveforms. After that, we give the invariances to them, and we can effectively obtain the SBD between every two application power waveforms. Based on the SBD values, the local outlier factor (LOF) method can finally distinguish between normal application behaviors and anomalous application behaviors. Experimental results demonstrate that the proposed method successfully detects anomalous application behaviors, while the existing state-of-the-art method fails to detect them.

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Section: Effect Of Invariances On Detection Results (Application 2)mentioning

confidence: 96%