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Hardware Trojan Detection and Classification Based on Logic Testing Utilizing Steady State Learning
Masaru OYA Masao YANAGISAWA Nozomu TOGAWA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2018/12/01
Online ISSN: 1745-1337
Type of Manuscript: Special Section PAPER (Special Section on VLSI Design and CAD Algorithms)
hardware Trojans, gate-level netlist, steady state, signal transition, logic test,
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Modern digital integrated circuits (ICs) are often designed and fabricated by third parties and tools, which can make IC design/fabrication vulnerable to malicious modifications. The malicious circuits are generally referred to as hardware Trojans (HTs) and they are considered to be a serious security concern. In this paper, we propose a logic-testing based HT detection and classification method utilizing steady state learning. We first observe that HTs are hidden while applying random test patterns in a short time but most of them can be activated in a very long-term random circuit operation. Hence it is very natural that we learn steady signal-transition states of every suspicious Trojan net in a netlist by performing short-term random simulation. After that, we simulate or emulate the netlist in a very long time by giving random test patterns and obtain a set of signal-transition states. By discovering correlation between them, our method detects HTs and finds out its behavior. HTs sometimes do not affect primary outputs but just leak information over side channels. Our method can be successfully applied to those types of HTs. Experimental results demonstrate that our method can successfully identify all the real Trojan nets to be Trojan nets and all the normal nets to be normal nets, while other existing logic-testing HT detection methods cannot detect some of them. Moreover, our method can successfully detect HTs even if they are not really activated during long-term random simulation. Our method also correctly guesses the HT behavior utilizing signal transition learning.