An enhanced classification-based golden chips-free hardware Trojan detection technique

Abstract

Recently, integrated circuits (ICs) are becoming increasing vulnerable to hardware Trojans. Most of existing works require golden chips to provide references for hardware Trojan detection. However, obtaining a golden chip is extremely difficult or even not exists. This paper presents a novel automated hardware Trojan detection technique based on enhanced two-class classification while eliminating the need of golden chips after fabrication. We formulate the Trojan detection problem into a classification problem, and train the algorithms using simulated ICs during IC design flow. The algorithm will form a classifier which can automatically identify Trojan-free and Trojan-inserted ICs during test-time. Moreover, we propose several optional optimized methods to enhance the technique: 1) we propose adaptive iterative optimization of one algorithm by focusing on errors, in which the weight-adjusting are based on how successful the algorithm was in the previous iteration; 2) we analyze the misclassified ICs' numbers of certain algorithms and present the matched algorithm-pairs; 3) we alter the algorithms to take into account of the costs of making different detection decisions, called cost-sensitive detection; 4) we present the suitable algorithm settings against high level of process variations. Experiment results on benchmark circuits show that the proposed technique can detect both known Trojans and various unknown Trojans with high accuracy and recall (90%∼100%). Since we didn't add any extra circuit to the design, there is no overhead of this approach.