Socio-network Analysis of RTL Designs for Hardware Trojan Localization
This addresses hardware security threats from malicious logic insertion, offering an incremental improvement by extending current detection techniques for RTL designs.
The paper tackles the problem of detecting Hardware Trojans (HTs) in RTL designs by applying social network analysis to identify vulnerable nets for triggering and payload signals, achieving an average detection accuracy of 97.37% on 420 combinational HT instances.
The recent surge in hardware security is significant due to offshoring the proprietary Intellectual property (IP). One distinct dimension of the disruptive threat is malicious logic insertion, also known as Hardware Trojan (HT). HT subverts the normal operations of a device stealthily. The diversity in HTs activation mechanisms and their location in design brings no catch-all detection techniques. In this paper, we propose to leverage principle features of social network analysis to security analysis of Register Transfer Level (RTL) designs against HT. The approach is based on investigating design properties, and it extends the current detection techniques. In particular, we perform both node- and graph-level analysis to determine the direct and indirect interactions between nets in a design. This technique helps not only in finding vulnerable nets that can act as HT triggering signals but also their interactions to influence a particular net to act as HT payload signal. We experiment the technique on 420 combinational HT instances, and on average, we can detect both triggering and payload signals with accuracy up to 97.37%.