TY - GEN
T1 - Empirical Evaluation on Anomaly Behavior Detection for Low-Cost Micro-Controllers Utilizing Accurate Power Analysis
AU - Hasegawa, Kento
AU - Chikamatsu, Kiyoshi
AU - Togawa, Nozomu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Since hardware/software vendors produce their IoT products easily and inexpensively, they often outsource their designs to third-party vendors where malicious third-party vendors can have a chance to insert software Trojans as well as 'hardware Trojans' into their IoT devices. How to tackle the issue becomes a serious concern these days. In this paper, we propose an anomaly behavior detection method utilizing accurate power analysis for low-cost micro-controllers. Our method accurately measures power consumption of the target device, and then classifies its waveform into the sleep-mode part, in which a micro-controller saves power, and into the active-mode part, in which a micro-controller works in a normal operation. After that, we obtain the duration time and consumed power from each active-mode period as feature values. Finally, we detect abnormal behavior based on the obtained feature values utilizing an outlier detection method. In our experiments, we empirically evaluate the proposed method utilizing two types of micro-controllers, and the experimental results demonstrate that our proposed method successfully detects abnormal behaviors.
AB - Since hardware/software vendors produce their IoT products easily and inexpensively, they often outsource their designs to third-party vendors where malicious third-party vendors can have a chance to insert software Trojans as well as 'hardware Trojans' into their IoT devices. How to tackle the issue becomes a serious concern these days. In this paper, we propose an anomaly behavior detection method utilizing accurate power analysis for low-cost micro-controllers. Our method accurately measures power consumption of the target device, and then classifies its waveform into the sleep-mode part, in which a micro-controller saves power, and into the active-mode part, in which a micro-controller works in a normal operation. After that, we obtain the duration time and consumed power from each active-mode period as feature values. Finally, we detect abnormal behavior based on the obtained feature values utilizing an outlier detection method. In our experiments, we empirically evaluate the proposed method utilizing two types of micro-controllers, and the experimental results demonstrate that our proposed method successfully detects abnormal behaviors.
KW - accurate power analysis
KW - anomaly behavior
KW - malicious function
KW - micro-controller
KW - outlier detection
UR - http://www.scopus.com/inward/record.url?scp=85073733842&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073733842&partnerID=8YFLogxK
U2 - 10.1109/IOLTS.2019.8854456
DO - 10.1109/IOLTS.2019.8854456
M3 - Conference contribution
AN - SCOPUS:85073733842
T3 - 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design, IOLTS 2019
SP - 54
EP - 57
BT - 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design, IOLTS 2019
A2 - Gizopoulos, Dimitris
A2 - Alexandrescu, Dan
A2 - Papavramidou, Panagiota
A2 - Maniatakos, Michail
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2019
Y2 - 1 July 2019 through 3 July 2019
ER -