Hardware IP Classification through Weighted Characteristics

Abstract

Today’s business model for hardware designs frequently incorporates third-party Intellectual Property (IP) mainly due to economic motivations. However, allowing third-party involvement also increases the possibility of malicious attacks, such as hardware Trojan insertion, which is a particularly dangerous security threat because functional testing can often leave the Trojan undetected. This research provides an improvement on a Trojan detection method and tool known as Structural Checking which analyzes Register-Transfer Level (RTL) soft IPs. Given an unknown IP, the tool will break down the design and label ports and signals with assets. Analyzing the asset patterns reveals how the IP is structured and provides information about its overall functionality. The tool incorporates a library of known designs referred to as the Golden Reference Library (GRL). All entries in the library, grouped into known-clean and know-infested, are analyzed in the same manner. A weighted percent match for each library entry against the unknown IP is calculated. A report is generated detailing all mismatched locations where users need to take a closer look. Due to the structural variability of soft IP designs, it is vital to provide the best possible weighting to best match the unknown IP to the most similar library entry. This paper provides a statistical approach to finding the best weights to optimize the tool’s matching algorithm.