TY - GEN
T1 - Designing hardware trojans and their detection based on a SVM-based approach
AU - Inoue, Tomotaka
AU - Hasegawa, Kento
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
N1 - Funding Information:
This paper is based on the results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Since hardware production become inexpensive and international, hardware vendors often outsource their products to third-party vendors. Due to the situation, malicious vendors can easily insert malfunctions (also known as 'hardware Trojans') to their products. In this paper, we experimentally evaluate a machine-learning-based hardware-Trojan detection method using several hardware Trojans we designed. To begin with, we design three types of hardware Trojans and insert them to simple RS232 transceiver circuits. After that, we learn known netlists, where we know which nets are Trojan ones or normal ones beforehand, using a machine-learning-based hardware-Trojan detection method with a support vector machine (SVM) classifier. Finally, we classify the nets in the designed hardware-Trojan-inserted netlists into a set of Trojan nets and that of normal nets using the learned classifier. The experimental results demonstrate that the hardware-Trojan detection method with the SVM-based approach can detect a part of hardware Trojans we designed.
AB - Since hardware production become inexpensive and international, hardware vendors often outsource their products to third-party vendors. Due to the situation, malicious vendors can easily insert malfunctions (also known as 'hardware Trojans') to their products. In this paper, we experimentally evaluate a machine-learning-based hardware-Trojan detection method using several hardware Trojans we designed. To begin with, we design three types of hardware Trojans and insert them to simple RS232 transceiver circuits. After that, we learn known netlists, where we know which nets are Trojan ones or normal ones beforehand, using a machine-learning-based hardware-Trojan detection method with a support vector machine (SVM) classifier. Finally, we classify the nets in the designed hardware-Trojan-inserted netlists into a set of Trojan nets and that of normal nets using the learned classifier. The experimental results demonstrate that the hardware-Trojan detection method with the SVM-based approach can detect a part of hardware Trojans we designed.
KW - Design time
KW - Gate-level netlist
KW - Hardware Trojan
KW - Machine learning
KW - Support vector machine
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U2 - 10.1109/ASICON.2017.8252600
DO - 10.1109/ASICON.2017.8252600
M3 - Conference contribution
AN - SCOPUS:85044751081
T3 - Proceedings of International Conference on ASIC
SP - 811
EP - 814
BT - Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017
A2 - Qin, Yajie
A2 - Hong, Zhiliang
A2 - Tang, Ting-Ao
PB - IEEE Computer Society
T2 - 12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017
Y2 - 25 October 2017 through 28 October 2017
ER -