A Side-Channel Hardware Trojan Detection Method Based on Fuzzy C-Means Clustering and Fusion Distance Algorithms

With the wide application of the Internet of Things technology, the hardware security has attracted more and more attention from users around the world. Hardware Trojan (HT) of integrated circuit (IC) has become a main security threat gradually. Therefore, HT detection is very significant. In this article, a HT automatic test system used for side-channel test combined logic test is constructed with a high-performance oscilloscope, FPGA chips, a NI digital acquisition card and LabVIEW software. Besides, the test flowchart and data processing method are depicted in detail. Spectral feature analysis combined principal component analysis is proposed for feature extraction. Fuzzy C-means clustering combined spectral energy analysis is put forward to distinguish the Trojan category from the golden category. Then Fusion distance (i.e., Mahalanobis distance combined Euclidean distance) is presented for the real-time HT recognition. A 128-bit AES cipher circuit and a 2-bit counter are applied as a golden circuit and a Trojan circuit, respectively. Experimental results demonstrate that the detection accuracy is 100% and the proposed detection method can easily achieve 0.1% HT detection sensitivity, which verifies that the detection method is feasible and effective.