TY - GEN
T1 - Building Trusted Golden Models-Free Hardware Trojan Detection Framework Against Untrustworthy Testing Parties Using a Novel Clustering Ensemble Technique
AU - Bian, Rongzhen
AU - Xue, Mingfu
AU - Wang, Jian
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/5
Y1 - 2018/9/5
N2 - As a result of the globalization of integrated circuits (ICs) design and fabrication process, ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works suppose that the testing stage is trustworthy. However, testing parties may conspire with malicious attackers to modify the results of hardware Trojan detection. In this paper, we propose a trusted and robust hardware Trojan detection framework against untrustworthy testing parties exploiting a novel clustering ensemble method. The proposed technique can expose the malicious modifications on Trojan detection results introduced by untrustworthy testing parties. Compared with the state-of-the-art detection methods, the proposed technique does not require fabricated golden chips or simulated golden models. The experiment results on ISCAS89 benchmark circuits show that the proposed technique can resist modifications robustly and detect hardware Trojans with decent accuracy (up to 91%).
AB - As a result of the globalization of integrated circuits (ICs) design and fabrication process, ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works suppose that the testing stage is trustworthy. However, testing parties may conspire with malicious attackers to modify the results of hardware Trojan detection. In this paper, we propose a trusted and robust hardware Trojan detection framework against untrustworthy testing parties exploiting a novel clustering ensemble method. The proposed technique can expose the malicious modifications on Trojan detection results introduced by untrustworthy testing parties. Compared with the state-of-the-art detection methods, the proposed technique does not require fabricated golden chips or simulated golden models. The experiment results on ISCAS89 benchmark circuits show that the proposed technique can resist modifications robustly and detect hardware Trojans with decent accuracy (up to 91%).
KW - clustering ensemble
KW - hardware Trojan detection
KW - hardware security
KW - unsupervised learning
KW - untrustworthy testing party
UR - https://www.scopus.com/pages/publications/85054090284
U2 - 10.1109/TrustCom/BigDataSE.2018.00203
DO - 10.1109/TrustCom/BigDataSE.2018.00203
M3 - 会议稿件
AN - SCOPUS:85054090284
SN - 9781538643877
T3 - Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
SP - 1458
EP - 1463
BT - Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
Y2 - 31 July 2018 through 3 August 2018
ER -