Increasing Security and Trust in HDL IP Through Evolutionary Computing
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| 发表在: | ProQuest Dissertations and Theses (2022) |
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ProQuest Dissertations & Theses
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| 在线阅读: | Citation/Abstract Full Text - PDF |
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| 100 | 1 | |a King, Bayley | |
| 245 | 1 | |a Increasing Security and Trust in HDL IP Through Evolutionary Computing | |
| 260 | |b ProQuest Dissertations & Theses |c 2022 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a The work shown in this study demonstrates how Evolutionary Computing (EC) can be used to add trust to Hardware Design Language (HDL) Intellectual Property (IP). HDL IP is often obtained through a 3rd party source due to time and cost constraints, in turn the IP is then considered untrusted by designers. These 3rd party IP could be infected with malicious additions, like Hardware Trojans (HT), or other damaging modifications. HT can often go undetected through standard detection techniques, but even if a designer can identify that there is something wrong with their design, how do they go about repairing it? We propose a study to investigate the ability to remove HT, investigate the use of partial test cases for evolution, and comment on the scalability of the approach. The authors then propose PyGenP, a Genetic Programming (GP) network written in Python, that allows for fast and quick evolution of HDL programs. A Hybrid Memetic GP algorithms that modify the population initialization function is then shown to offer an improvement over traditional GP, while generating better low-order schemas. Finally, we propose an algorithm, using this Hybrid Memetic Genetic Programming initialization function, to perform Targeted Evolution, on select portions of am HDL program, and comment on the improvements the algorithm offers over traditional GP. The authors then close by giving a retrospect of the work completed, and offer recommendations for future work.  | |
| 653 | |a Computer science | ||
| 653 | |a Computer engineering | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Electrical engineering | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2022) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2781100594/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2781100594/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |