TY - GEN
T1 - Hardware Trojans classification for gate-level netlists using multi-layer neural networks
AU - Hasegawa, Kento
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/19
Y1 - 2017/9/19
N2 - Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.
AB - Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.
KW - Gate-level netlist
KW - Hardware Trojan
KW - Machine learning
KW - Multi-layer
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=85032723104&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032723104&partnerID=8YFLogxK
U2 - 10.1109/IOLTS.2017.8046227
DO - 10.1109/IOLTS.2017.8046227
M3 - Conference contribution
AN - SCOPUS:85032723104
T3 - 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
SP - 227
EP - 232
BT - 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
Y2 - 3 July 2017 through 5 July 2017
ER -