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Improving Hardware Trojan Detection Coverage by Utilizing Features at Different Abstraction Levels
http://hdl.handle.net/10061/0002000519
http://hdl.handle.net/10061/0002000519959f12cf-0266-4d35-8660-14a4daed8716
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-08-09 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Improving Hardware Trojan Detection Coverage by Utilizing Features at Different Abstraction Levels | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Hardware Trojan detection | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | machine learning | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | integrated circuit | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | register-transfer level | |||||||||||||
| キーワード | ||||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | gate level | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ | journal article | |||||||||||||
| アクセス権 | ||||||||||||||
| アクセス権 | open access | |||||||||||||
| 著者 |
Choo, Hau Sim
× Choo, Hau Sim
× Ooi, Chia Yee
× Ismail, Nordinah
× 井上, 美智子× Kok, Chee Hoo
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | In this paper, we introduced a solution to improve hardware Trojan (HT) detection coverage by analyzing features at different abstraction levels. We demonstrated our solution with a supervised classification of HT branching statement (BS) in register-transfer-level (RTL) description. The proposed classifier was trained with a double-abstraction-level feature vector consisting of features extracted at RTL and gate level (GL). In the experiment, we evaluated the HT detection coverage of the trained classifier by applying them on 24 self-designed HT circuits. The proposed classifier achieved the highest 87.5% HT detection coverage with 81.25% true positive rate (TPR), 88.44% true negative rate (TNR), and 88.24% accuracy (ACC). The result proved that the double-abstraction-level feature vector outperformed the single-abstraction-level feature vector with a higher HT detection coverage. | |||||||||||||
| 書誌情報 |
en : Journal of Advanced Research in Applied Sciences and Engineering Technology 巻 32, 号 1, p. 73-86, 発行日 2023-08-30 |
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| 出版者 | ||||||||||||||
| 出版者 | Semarak Ilmu Publishing | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2462-1943 | |||||||||||||
| 出版者版DOI | ||||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.37934/araset.32.1.7386 | |||||||||||||
| 出版者版URI | ||||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||||
| 識別子タイプ | URI | |||||||||||||
| 関連識別子 | https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/1688 | |||||||||||||
| 権利 | ||||||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by-nc/4.0/ | |||||||||||||
| 権利情報 | Beginning 2021, Journal of Advanced Research in Applied Sciences and Engineering Technology is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. | |||||||||||||
| 著者版フラグ | ||||||||||||||
| 出版タイプ | NA | |||||||||||||
