Graph Features Analysis and Detection Method of IP Soft Core Hardware Trojan

NI Lin; LI Lin; ZHANG Shuai; TONG Sicheng; QIAN Yang

doi:10.11999/JEIT240219

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NI Lin, LI Lin, ZHANG Shuai, TONG Sicheng, QIAN Yang. Graph Features Analysis and Detection Method of IP Soft Core Hardware Trojan[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240219

Citation:

NI Lin, LI Lin, ZHANG Shuai, TONG Sicheng, QIAN Yang. Graph Features Analysis and Detection Method of IP Soft Core Hardware Trojan[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240219

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Graph Features Analysis and Detection Method of IP Soft Core Hardware Trojan

doi: 10.11999/JEIT240219

NI Lin^{1, 3},
LI Lin³,
ZHANG Shuai^{2, 3
,
,},
TONG Sicheng³,
QIAN Yang³

1.
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
2.
College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China
3.
College of Information and Communication, National University of Defense Technology, Wuhan 430035, China

Received Date: 2024-03-29
Rev Recd Date: 2024-09-05

Available Online: 2024-09-28

Abstract

Abstract

With the rapid development of integrated circuit technology, chips are easily implanted with malicious hardware Trojan logic in the process of design, production and packaging. Current security detection methods for IP soft core are logically complex, prone to errors and omissions, and unable to detect encrypted IP soft core. The paper uses the feature differences of non-controllable IP soft core and hardware Trojan Register Transfer Level (RTL) code grayscale map, proposing a hardware Trojan detection method for IP soft cores based on graph feature analysis, through the map conversion and map enhancement to get the standard map, using the texture feature extraction matching algorithm to achieve hardware Trojan detection. The experimental subjects are functional logic units with seven types of typical Trojans implanted during the design phase, and the detection results show that the detection correct rate of seven types of typical hardware Trojans has reached more than 90%, and the average growth rate of the number of successful feature point matches after the image enhancement has reached 13.24%, effectively improving the effectiveness of hardware Trojan detection.
- IP soft core,
- Hardware Trojan,
- Grayscale map,
- Texture feature,
- Detection algorithm

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References(16)

References

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