IEICE TRANSACTIONS on Fundamentals
An Anomalous Behavior Detection Method Utilizing Extracted Application-Specific Power Behaviors
Summary :
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 behaviors, 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 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. We conduct two experiments to show how our proposed method works: one runs three application programs and an anomalous application program randomly and the other runs three application programs in series and an anomalous application program very rarely. Application programs on both experiments are implemented on a single board computer. The experimental results demonstrate that the proposed method successfully detects anomalous behaviors by extracting application-specific power behaviors, while the existing approaches cannot.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.11 pp.1555-1565
- Publication Date
- 2021/11/01
- Publicized
- 2021/07/08
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020KEP0001
- Type of Manuscript
- Special Section PAPER (Special Section on Circuits and Systems)
- Category
Authors
Kazunari TAKASAKI
Waseda University
Ryoichi KIDA
LAC Co., Ltd.
Nozomu TOGAWA
Waseda University
Keyword
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Kazunari TAKASAKI, Ryoichi KIDA, Nozomu TOGAWA, "An Anomalous Behavior Detection Method Utilizing Extracted Application-Specific Power Behaviors" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 11, pp. 1555-1565, November 2021, doi: 10.1587/transfun.2020KEP0001.
Abstract: 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 behaviors, 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 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. We conduct two experiments to show how our proposed method works: one runs three application programs and an anomalous application program randomly and the other runs three application programs in series and an anomalous application program very rarely. Application programs on both experiments are implemented on a single board computer. The experimental results demonstrate that the proposed method successfully detects anomalous behaviors by extracting application-specific power behaviors, while the existing approaches cannot.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020KEP0001/_p
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@ARTICLE{e104-a_11_1555,
author={Kazunari TAKASAKI, Ryoichi KIDA, Nozomu TOGAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Anomalous Behavior Detection Method Utilizing Extracted Application-Specific Power Behaviors},
year={2021},
volume={E104-A},
number={11},
pages={1555-1565},
abstract={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 behaviors, 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 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. We conduct two experiments to show how our proposed method works: one runs three application programs and an anomalous application program randomly and the other runs three application programs in series and an anomalous application program very rarely. Application programs on both experiments are implemented on a single board computer. The experimental results demonstrate that the proposed method successfully detects anomalous behaviors by extracting application-specific power behaviors, while the existing approaches cannot.},
keywords={},
doi={10.1587/transfun.2020KEP0001},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - An Anomalous Behavior Detection Method Utilizing Extracted Application-Specific Power Behaviors
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1555
EP - 1565
AU - Kazunari TAKASAKI
AU - Ryoichi KIDA
AU - Nozomu TOGAWA
PY - 2021
DO - 10.1587/transfun.2020KEP0001
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2021
AB - 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 behaviors, 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 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. We conduct two experiments to show how our proposed method works: one runs three application programs and an anomalous application program randomly and the other runs three application programs in series and an anomalous application program very rarely. Application programs on both experiments are implemented on a single board computer. The experimental results demonstrate that the proposed method successfully detects anomalous behaviors by extracting application-specific power behaviors, while the existing approaches cannot.
ER -
English
