GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS
A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being mad...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/2/245 |
| _version_ | 1797494510534524928 |
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| author | Konstantinos G. Liakos Georgios K. Georgakilas Fotis C. Plessas Paris Kitsos |
| author_facet | Konstantinos G. Liakos Georgios K. Georgakilas Fotis C. Plessas Paris Kitsos |
| author_sort | Konstantinos G. Liakos |
| collection | DOAJ |
| description | A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB. |
| first_indexed | 2024-03-10T01:35:20Z |
| format | Article |
| id | doaj.art-3d0e32b8d0ec42feb6e4f0c75662f9f6 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T01:35:20Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-3d0e32b8d0ec42feb6e4f0c75662f9f62023-11-23T13:34:38ZengMDPI AGElectronics2079-92922022-01-0111224510.3390/electronics11020245GAINESIS: Generative Artificial Intelligence NEtlists SynthesISKonstantinos G. Liakos0Georgios K. Georgakilas1Fotis C. Plessas2Paris Kitsos3Electrical & Computer Engineering Department, University of Thessaly, 38334 Volos, GreeceElectrical & Computer Engineering Department, University of Thessaly, 38334 Volos, GreeceElectrical & Computer Engineering Department, University of Thessaly, 38334 Volos, GreeceElectrical & Computer Engineering Department, University of the Peloponnese, 26334 Patras, GreeceA significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB.https://www.mdpi.com/2079-9292/11/2/245artificial intelligencegenerative learninghardware trojanapplication-specific integrated circuitgate-level netlistssynthesis |
| spellingShingle | Konstantinos G. Liakos Georgios K. Georgakilas Fotis C. Plessas Paris Kitsos GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS Electronics artificial intelligence generative learning hardware trojan application-specific integrated circuit gate-level netlists synthesis |
| title | GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS |
| title_full | GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS |
| title_fullStr | GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS |
| title_full_unstemmed | GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS |
| title_short | GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS |
| title_sort | gainesis generative artificial intelligence netlists synthesis |
| topic | artificial intelligence generative learning hardware trojan application-specific integrated circuit gate-level netlists synthesis |
| url | https://www.mdpi.com/2079-9292/11/2/245 |
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