An Anomalous Behavior Detection Method Based on Power Analysis Utilizing Steady State Power Waveform Predicted by LSTM

Kazunari Takasaki, Ryoichi Kida, Nozomu Togawa

研究成果: Conference contribution

抄録

Hardware security issues have emerged in recent years as Internet of Things (IoT) devices have rapidly spread. Power analysis is one of the methods to detect anomalous operations, but it is hard to apply it to IoT devices where an operating system and various software programs are running and hence its power waveforms become more complex. In this paper, we propose an anomalous behavior detection method utilizing application-specific power behaviors extracted by steady-state power waveform, which is generated by LSTM (long short-term memory). The proposed method is based on extracting application-specific power behaviors by predicting steady-state power waveforms. At that time, by using LSTM, we can effectively predict steady-state power waveforms, even if they include one or more cycled waveforms and/or they are composed of many complex waveforms. In the experiment, we implement three normal application programs and one anomalous application program on a single board computer and apply the proposed method to it. The experimental results demonstrate that the proposed method successfully detects the anomalous power behavior of an anomalous application program, while the existing method cannot.

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665433709
DOI
出版ステータスPublished - 2021 6 28
イベント27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021 - Virtual, Online
継続期間: 2021 6 282021 6 30

出版物シリーズ

名前Proceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021

Conference

Conference27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
CityVirtual, Online
Period21/6/2821/6/30

ASJC Scopus subject areas

  • ソフトウェア
  • エネルギー工学および電力技術
  • 電子工学および電気工学
  • 安全性、リスク、信頼性、品質管理
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ

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