Visible to the public A Co-Training Based Hardware Trojan Detection Technique by Exploiting Unlabeled ICs and Inaccurate Simulation Models

TitleA Co-Training Based Hardware Trojan Detection Technique by Exploiting Unlabeled ICs and Inaccurate Simulation Models
Publication TypeConference Paper
Year of Publication2018
AuthorsXue, M., Bian, R., Wang, J., Liu, W.
Conference Name2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Date Publishedaug
ISBN Number978-1-5386-4388-4
Keywordsaccurate classification model, actual silicon fabrication, Classification algorithms, classification-based golden chips-free hardware Trojan detection technique, co-training, co-training based hardware Trojan detection technique, fabricated golden chips, fabrication, golden chip, Hardware, hardware security, hardware Trojan detection, IC labeling, inaccurate simulation models, Integrated circuit modeling, invasive software, learning (artificial intelligence), pattern classification, pubcrawl, reliable detection capability, Silicon, simulated IC, Training, trojan horse detection, Trojan horses, unlabeled fabricated IC, unlabeled IC, unlabeled ICs
Abstract

Integrated circuits (ICs) are becoming vulnerable to hardware Trojans. Most of existing works require golden chips to provide references for hardware Trojan detection. However, a golden chip is extremely difficult to obtain. In previous work, we have proposed a classification-based golden chips-free hardware Trojan detection technique. However, the algorithm in the previous work are trained by simulated ICs without considering that there may be a shift which occurs between the simulation and the silicon fabrication. It is necessary to learn from actual silicon fabrication in order to obtain an accurate and effective classification model. We propose a co-training based hardware Trojan detection technique exploiting unlabeled fabricated ICs and inaccurate simulation models, to provide reliable detection capability when facing fabricated ICs, while eliminating the need of fabricated golden chips. First, we train two classification algorithms using simulated ICs. During test-time, the two algorithms can identify different patterns in the unlabeled ICs, and thus be able to label some of these ICs for the further training of the another algorithm. Moreover, we use a statistical examination to choose ICs labeling for the another algorithm in order to help prevent a degradation in performance due to the increased noise in the labeled ICs. We also use a statistical technique for combining the hypotheses from the two classification algorithms to obtain the final decision. The theoretical basis of why the co-training method can work is also described. Experiment results on benchmark circuits show that the proposed technique can detect unknown Trojans with high accuracy (92% 97%) and recall (88% 95%).

URLhttps://ieeexplore.ieee.org/document/8456071
DOI10.1109/TrustCom/BigDataSE.2018.00202
Citation Keyxue_co-training_2018