An Anomalous Behavior Detection Method for IoT Devices by Extracting Application-Specific Power Behaviors

Kazunari Takasaki, Kento Hasegawa, Ryoichi Kida, Nozomu Togawa

研究成果: Conference contribution

抄録

With the widespread use of Internet of Things (IoT) devices in recent years, we utilize a variety of hardware devices in our daily life. On the other hand, hardware security issues are emerging. 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. In this paper, we propose an anomalous behavior detection method for an IoT device by extracting application-specific power behaviors. First, we measure a power consumption of an IoT device, and obtain the power waveform. Next, we extract an application-specific power waveform by eliminating a steady factor from the obtained power waveform. Finally, we extract feature values from the application-specific power waveform and detect an anomalous behavior by utilizing the local outlier factor (LOF) method. The experimental results using a single board computer demonstrate that the proposed method successfully detects the anomalous power behavior of an anomalous application program.

本文言語English
ホスト出版物のタイトルProceedings - 2020 26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728181875
DOI
出版ステータスPublished - 2020 7
イベント26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020 - Virtual, Online, Italy
継続期間: 2020 7 132020 7 16

出版物シリーズ

名前Proceedings - 2020 26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020

Conference

Conference26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020
CountryItaly
CityVirtual, Online
Period20/7/1320/7/16

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

フィンガープリント 「An Anomalous Behavior Detection Method for IoT Devices by Extracting Application-Specific Power Behaviors」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル