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An Anomalous Behavior Detection Method Utilizing IoT Power Waveform Shapes

Kota HISAFURU, Kazunari TAKASAKI, Nozomu TOGAWA

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Summary :

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 shape-based 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.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E107-A No.1 pp.75-86
Publication Date
2024/01/01
Publicized
2023/08/16
Online ISSN
1745-1337
DOI
10.1587/transfun.2023KEP0012
Type of Manuscript
Special Section PAPER (Special Section on Circuits and Systems)
Category

Authors

Kota HISAFURU
  Waseda University
Kazunari TAKASAKI
  Waseda University
Nozomu TOGAWA
  Waseda University

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