Detecting Vulnerability in Hardware Description Languages: Opcode Language Processing

Detecting vulnerable code blocks has become a highly popular topic in computer-aided design, especially with the advancement of natural language processing (NLP). Analyzing hardware description languages (
HDLs
), such as Verilog, involves dealing with lengthy code. This letter introduces an innovative identification of attack-vulnerable hardware by the use of
opcode
processing. Leveraging the advantage of architecturally defined
opcodes
and expressing all operations at the beginning of each code line, the word processing problem is efficiently transformed into
opcode
processing. This research converts a benchmark dataset into an intermediary code stack, subsequently classifying secure and fragile codes using NLP techniques. The results reveal a framework that achieves up to 94% accuracy when employing sophisticated convolutional neural networks (CNNs) architecture with extra embedding layers. Thus, it provides a means for users to quickly verify the vulnerability of their
HDL
code by inspecting a supervised learning model trained on the predefined vulnerabilities. It also supports the superior efficacy of
opcode
-based processing in Trojan detection by analyzing the outcomes derived from a model trained using the
HDL
dataset.