Socio-network Analysis of RTL Designs for Hardware Trojan Localization

TL;DR

This paper introduces a novel social network analysis-based method for detecting hardware Trojans in RTL designs, achieving high accuracy in identifying malicious signals and their interactions.

Contribution

It extends existing detection techniques by applying social network analysis to RTL design properties for hardware Trojan localization.

Findings

Achieves up to 97.37% accuracy in detecting Trojan signals.

Effectively identifies both triggering and payload signals.

Demonstrates scalability on 420 Trojan instances.

Abstract

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%.