A Multi-Dimensional Hardware Trojan Design Platform to Enhance Hardware Security

The efficiency and capability of an hardware trojan (HT) detection process are highly dependent on the quality of HT benchmarks for HT detection; but a limited number of HT models are available and most of them are alike in terms of their activation mechanism and malicious function. To address this issue, this paper proposes a novel kind of HT design named multi-dimensional hardware Trojans (MDHTs) and develops a method to generate configurable MDHT benchmark platform. The proposed MDHT circuits include multiple net(s) as trigger signals from each of the rarely activated, highly activated, and partially activated categories to increase MDHT’s adverse effects. The generated MDHT-infected circuits are tested by an unsupervised machine learning based HT detection technique-Controllability and Observability for HT Detection (COTD). Experimentation on ISCAS benchmarks ensures that the detection method is unable to detect the developed MDHT circuits as the nets belong to higher and partial activities are creating at least 50% and at most 80% false negative rate which validates the MDHT insertion framework in addition to the available HT benchmarks.