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

Kazunari Takasaki, Ryoichi Kida, Nozomu Togawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design, IOLTS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433709
DOIs
Publication statusPublished - 2021 Jun 28
Event27th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2021 - Virtual, Online
Duration: 2021 Jun 282021 Jun 30

Publication series

NameProceedings - 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

Keywords

  • anomalous behavior
  • IoT device
  • LSTM
  • power analysis

ASJC Scopus subject areas

  • Software
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture

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