{"title":"Natural Language Processing for Hardware Security: Case of Hardware Trojan Detection in FPGAs","authors":"Jaya Dofe, Wafi Danesh, Vaishnavi More, Aaditya Chaudhari","doi":"10.3390/cryptography8030036","DOIUrl":null,"url":null,"abstract":"Field-programmable gate arrays (FPGAs) offer the inherent ability to reconfigure at runtime, making them ideal for applications such as data centers, cloud computing, and edge computing. This reconfiguration, often achieved through remote access, enables efficient resource utilization but also introduces critical security vulnerabilities. An adversary could exploit this access to insert a dormant hardware trojan (HT) into the configuration bitstream, bypassing conventional security and verification measures. To address this security threat, we propose a supervised learning approach using deep recurrent neural networks (RNNs) for HT detection within FPGA configuration bitstreams. We explore two RNN architectures: basic RNN and long short-term memory (LSTM) networks. Our proposed method analyzes bitstream patterns, to identify anomalies indicative of malicious modifications. We evaluated the effectiveness on ISCAS 85 benchmark circuits of varying sizes and topologies, implemented on a Xilinx Artix-7 FPGA. The experimental results revealed that the basic RNN model showed lower accuracy in identifying HT-compromised bitstreams for most circuits. In contrast, the LSTM model achieved a significantly higher average accuracy of 93.5%. These results demonstrate that the LSTM model is more successful for HT detection in FPGA bitstreams. This research paves the way for using RNN architectures for HT detection in FPGAs, eliminating the need for time-consuming and resource-intensive reverse engineering or performance-degrading bitstream conversions.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"5 1","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/cryptography8030036","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Field-programmable gate arrays (FPGAs) offer the inherent ability to reconfigure at runtime, making them ideal for applications such as data centers, cloud computing, and edge computing. This reconfiguration, often achieved through remote access, enables efficient resource utilization but also introduces critical security vulnerabilities. An adversary could exploit this access to insert a dormant hardware trojan (HT) into the configuration bitstream, bypassing conventional security and verification measures. To address this security threat, we propose a supervised learning approach using deep recurrent neural networks (RNNs) for HT detection within FPGA configuration bitstreams. We explore two RNN architectures: basic RNN and long short-term memory (LSTM) networks. Our proposed method analyzes bitstream patterns, to identify anomalies indicative of malicious modifications. We evaluated the effectiveness on ISCAS 85 benchmark circuits of varying sizes and topologies, implemented on a Xilinx Artix-7 FPGA. The experimental results revealed that the basic RNN model showed lower accuracy in identifying HT-compromised bitstreams for most circuits. In contrast, the LSTM model achieved a significantly higher average accuracy of 93.5%. These results demonstrate that the LSTM model is more successful for HT detection in FPGA bitstreams. This research paves the way for using RNN architectures for HT detection in FPGAs, eliminating the need for time-consuming and resource-intensive reverse engineering or performance-degrading bitstream conversions.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.