Hardware Trojan vulnerability assessment in digital integrated circuits using learnable classifiers
Abstract- In the current distributed integrated circuits (IC) industry, the possibility of adversarial hardware attacks cannot be ignored. Hardware Trojans (HT) attacks may lead to information leakage or failure in security-critical systems. The wide range of HT types and related insertion strategie...
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Amirkabir University of Technology
2024-07-01
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| Series: | AUT Journal of Electrical Engineering |
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| Online Access: | https://eej.aut.ac.ir/article_5393_c9d2a57a04d56d193c26f4192be51233.pdf |
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| author | Hadi Jahanirad Mohammad Fathi |
| author_facet | Hadi Jahanirad Mohammad Fathi |
| author_sort | Hadi Jahanirad |
| collection | DOAJ |
| description | Abstract- In the current distributed integrated circuits (IC) industry, the possibility of adversarial hardware attacks cannot be ignored. Hardware Trojans (HT) attacks may lead to information leakage or failure in security-critical systems. The wide range of HT types and related insertion strategies makes the HT detection process very complex. Consequently, developing IC design methodologies that are robust against HT insertion would be of great merit. To measure the HT robustness, a vulnerability analysis of the proposed circuits should be performed which involves several interrelated factors (e.g. the layout of white spaces distribution, the unutilized routing resources, the activity of the circuit nodes, the delay values of circuit paths, etc.). In this paper, a novel framework is proposed to classify the IC vulnerability level. First, a comprehensive dataset is generated considering different HTs insertion into the ISCAS 85 and ISCAS 89 benchmark circuits. Then extraction of efficient features from the input image is accomplished by pre-trained deep neural networks. Finally, the vulnerability level (which is defined as low vulnerable, moderately vulnerable, and highly vulnerable) of every circuit is extracted using various trained classifiers (Ensemble, SVM, Naïve Bayes, and KNN). Simulation results confirm a 25% improvement in classification accuracy in the most successful classifier (97%) compared with the most successful previous study (72%). |
| format | Article |
| id | doaj-art-efb4f9d2b7564b02a4619963dd1ee3c1 |
| institution | Kabale University |
| issn | 2588-2910 2588-2929 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Amirkabir University of Technology |
| record_format | Article |
| series | AUT Journal of Electrical Engineering |
| spelling | doaj-art-efb4f9d2b7564b02a4619963dd1ee3c12025-08-20T03:26:44ZengAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-29102588-29292024-07-0156341943810.22060/eej.2024.22910.55725393Hardware Trojan vulnerability assessment in digital integrated circuits using learnable classifiersHadi Jahanirad0Mohammad Fathi1Department of Electronics and Communication Engineering, University of Kurdistan, Sanandaj, Kurdistan, IranDepartment of Electronics and Communication Engineering, University of Kurdistan, Sanandaj, Kurdistan, IranAbstract- In the current distributed integrated circuits (IC) industry, the possibility of adversarial hardware attacks cannot be ignored. Hardware Trojans (HT) attacks may lead to information leakage or failure in security-critical systems. The wide range of HT types and related insertion strategies makes the HT detection process very complex. Consequently, developing IC design methodologies that are robust against HT insertion would be of great merit. To measure the HT robustness, a vulnerability analysis of the proposed circuits should be performed which involves several interrelated factors (e.g. the layout of white spaces distribution, the unutilized routing resources, the activity of the circuit nodes, the delay values of circuit paths, etc.). In this paper, a novel framework is proposed to classify the IC vulnerability level. First, a comprehensive dataset is generated considering different HTs insertion into the ISCAS 85 and ISCAS 89 benchmark circuits. Then extraction of efficient features from the input image is accomplished by pre-trained deep neural networks. Finally, the vulnerability level (which is defined as low vulnerable, moderately vulnerable, and highly vulnerable) of every circuit is extracted using various trained classifiers (Ensemble, SVM, Naïve Bayes, and KNN). Simulation results confirm a 25% improvement in classification accuracy in the most successful classifier (97%) compared with the most successful previous study (72%).https://eej.aut.ac.ir/article_5393_c9d2a57a04d56d193c26f4192be51233.pdfensemble learninglearnable classifiersdeep neural networksdigital circuitsvulnerability analysishardware trojans |
| spellingShingle | Hadi Jahanirad Mohammad Fathi Hardware Trojan vulnerability assessment in digital integrated circuits using learnable classifiers AUT Journal of Electrical Engineering ensemble learning learnable classifiers deep neural networks digital circuits vulnerability analysis hardware trojans |
| title | Hardware Trojan vulnerability assessment in digital integrated circuits using learnable classifiers |
| title_full | Hardware Trojan vulnerability assessment in digital integrated circuits using learnable classifiers |
| title_fullStr | Hardware Trojan vulnerability assessment in digital integrated circuits using learnable classifiers |
| title_full_unstemmed | Hardware Trojan vulnerability assessment in digital integrated circuits using learnable classifiers |
| title_short | Hardware Trojan vulnerability assessment in digital integrated circuits using learnable classifiers |
| title_sort | hardware trojan vulnerability assessment in digital integrated circuits using learnable classifiers |
| topic | ensemble learning learnable classifiers deep neural networks digital circuits vulnerability analysis hardware trojans |
| url | https://eej.aut.ac.ir/article_5393_c9d2a57a04d56d193c26f4192be51233.pdf |
| work_keys_str_mv | AT hadijahanirad hardwaretrojanvulnerabilityassessmentindigitalintegratedcircuitsusinglearnableclassifiers AT mohammadfathi hardwaretrojanvulnerabilityassessmentindigitalintegratedcircuitsusinglearnableclassifiers |