Open AccessResearch on the Detection Method of HOG-SVM for Doping Modified Hardware Trojan
Author(s) -
Chaolong Sun,
Lei Li,
Wanting Zhou,
Xianjun Tan,
Yuanhang He
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/3/032086
Subject(s) - support vector machine , histogram , computer science , trojan , histogram of oriented gradients , set (abstract data type) , pattern recognition (psychology) , polarity (international relations) , artificial intelligence , computer hardware , image (mathematics) , chemistry , computer security , biochemistry , cell , programming language
With the globalization of the IC industry, hardware security has become an important topic, especially hardware Trojans (HT) hidden in ICs. In this paper, we presented the method based on histogram of oriented gradient (HOG) and support vector machine (SVM) to detect doping modified HTs, which is generally inserted by modifying the layout of some standard cells and is very difficult to detect. In order to address this problem, this paper firstly takes the standard layout without changing the doping polarity as the Trojan free (TF) layout, and regards the modified layout with changing the doping polarity as the HT layout, and then classifies the experimental data set into HT or TF by extracting the HOG features and using SVM training classification method. Experimental results demonstrate that the experimental accuracy of this method can reach 92.8%.