Hardware Trojan detection and classification based on steady state learning

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. 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.