TY - GEN
T1 - A hardware-Trojan classification method utilizing boundary net structures
AU - Hasegawa, Kento
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/3/26
Y1 - 2018/3/26
N2 - Recently, cybersecurity has become a serious concern for us. For example, the threats of hardware Trojans (malfunctions inserted into hardware devices) have appeared. Since hardware vendors often outsource parts of their hardware products to third-party vendors, the risk of hardware-Trojan insertion has been increased. Especially in the hardware design step, malicious vendors have a chance to insert hardware Trojans easily. In this paper, we propose a hardware-Trojan classification method utilizing boundary net structures. To begin with, we use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and that of Trojan nets. Based on the classification, we investigate the nets around the boundary between normal nets and Trojan nets and extract the features of the nets identified to be normal nets or Trojan nets mistakenly. Finally, using the classification results of machine-learning-based hardware-Trojan detection and the extracted features of the boundary nets, we classify the nets in a given netlist into a set of normal nets and that of Trojan nets again. The experimental results demonstrate that our method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of true positive rate.
AB - Recently, cybersecurity has become a serious concern for us. For example, the threats of hardware Trojans (malfunctions inserted into hardware devices) have appeared. Since hardware vendors often outsource parts of their hardware products to third-party vendors, the risk of hardware-Trojan insertion has been increased. Especially in the hardware design step, malicious vendors have a chance to insert hardware Trojans easily. In this paper, we propose a hardware-Trojan classification method utilizing boundary net structures. To begin with, we use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and that of Trojan nets. Based on the classification, we investigate the nets around the boundary between normal nets and Trojan nets and extract the features of the nets identified to be normal nets or Trojan nets mistakenly. Finally, using the classification results of machine-learning-based hardware-Trojan detection and the extracted features of the boundary nets, we classify the nets in a given netlist into a set of normal nets and that of Trojan nets again. The experimental results demonstrate that our method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of true positive rate.
KW - Trojan feature
KW - boundary nets
KW - gate-level netlist
KW - hardware Trojan
KW - hardware design
UR - http://www.scopus.com/inward/record.url?scp=85048750003&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048750003&partnerID=8YFLogxK
U2 - 10.1109/ICCE.2018.8326247
DO - 10.1109/ICCE.2018.8326247
M3 - Conference contribution
AN - SCOPUS:85048750003
T3 - 2018 IEEE International Conference on Consumer Electronics, ICCE 2018
SP - 1
EP - 4
BT - 2018 IEEE International Conference on Consumer Electronics, ICCE 2018
A2 - Mohanty, Saraju P.
A2 - Corcoran, Peter
A2 - Li, Hai
A2 - Sengupta, Anirban
A2 - Lee, Jong-Hyouk
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Consumer Electronics, ICCE 2018
Y2 - 12 January 2018 through 14 January 2018
ER -