Abstract
Recently, due to the increase of outsourcing in IC design, it has been reported that malicious third-party vendors often insert hardware Trojans into their ICs. How to detect them is a strong concern in IC design process. The features of hardware-Trojan infected nets (or Trojan nets) in ICs often differ from those of normal nets. To classify all the nets in netlists designed by third-party vendors into Trojan ones and normal ones, we have to extract effective Trojan features from Trojan nets. In this paper, we first propose 51 Trojan features which describe Trojan nets from netlists. Based on the importance values obtained from the random forest classifier, we extract the best set of 11 Trojan features out of the 51 features which can effectively detect Trojan nets, maximizing the F-measures. By using the 11 Trojan features extracted, the machine-learning based hardware Trojan classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several TrustHUB benchmarks and obtained the average F-measure of 74.6%, which realizes the best values among existing machine-learning-based hardware-Trojan detection methods.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Symposium on Circuits and Systems |
| Subtitle of host publication | From Dreams to Innovation, ISCAS 2017 - Conference Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781467368520 |
| DOIs | |
| Publication status | Published - 2017 Sep 25 |
| Event | 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States Duration: 2017 May 28 → 2017 May 31 |
Other
| Other | 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 |
|---|---|
| Country | United States |
| City | Baltimore |
| Period | 17/5/28 → 17/5/31 |
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Keywords
- F-measure
- gate-level netlist
- hardware Trojan
- machine learning
- random forest
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Trojan-feature extraction at gate-level netlists and its application to hardware-Trojan detection using random forest classifier. / Hasegawa, Kento; Yanagisawa, Masao; Togawa, Nozomu.
IEEE International Symposium on Circuits and Systems: From Dreams to Innovation, ISCAS 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 8050827.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Trojan-feature extraction at gate-level netlists and its application to hardware-Trojan detection using random forest classifier
AU - Hasegawa, Kento
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
PY - 2017/9/25
Y1 - 2017/9/25
N2 - Recently, due to the increase of outsourcing in IC design, it has been reported that malicious third-party vendors often insert hardware Trojans into their ICs. How to detect them is a strong concern in IC design process. The features of hardware-Trojan infected nets (or Trojan nets) in ICs often differ from those of normal nets. To classify all the nets in netlists designed by third-party vendors into Trojan ones and normal ones, we have to extract effective Trojan features from Trojan nets. In this paper, we first propose 51 Trojan features which describe Trojan nets from netlists. Based on the importance values obtained from the random forest classifier, we extract the best set of 11 Trojan features out of the 51 features which can effectively detect Trojan nets, maximizing the F-measures. By using the 11 Trojan features extracted, the machine-learning based hardware Trojan classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several TrustHUB benchmarks and obtained the average F-measure of 74.6%, which realizes the best values among existing machine-learning-based hardware-Trojan detection methods.
AB - Recently, due to the increase of outsourcing in IC design, it has been reported that malicious third-party vendors often insert hardware Trojans into their ICs. How to detect them is a strong concern in IC design process. The features of hardware-Trojan infected nets (or Trojan nets) in ICs often differ from those of normal nets. To classify all the nets in netlists designed by third-party vendors into Trojan ones and normal ones, we have to extract effective Trojan features from Trojan nets. In this paper, we first propose 51 Trojan features which describe Trojan nets from netlists. Based on the importance values obtained from the random forest classifier, we extract the best set of 11 Trojan features out of the 51 features which can effectively detect Trojan nets, maximizing the F-measures. By using the 11 Trojan features extracted, the machine-learning based hardware Trojan classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several TrustHUB benchmarks and obtained the average F-measure of 74.6%, which realizes the best values among existing machine-learning-based hardware-Trojan detection methods.
KW - F-measure
KW - gate-level netlist
KW - hardware Trojan
KW - machine learning
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85032655111&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032655111&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2017.8050827
DO - 10.1109/ISCAS.2017.8050827
M3 - Conference contribution
BT - IEEE International Symposium on Circuits and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
ER -