The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Baru-baru ini, dengan penyebaran peranti Internet of Things (IoT), peranti perkakasan terbenam telah digunakan dalam pelbagai barangan elektrik harian. Disebabkan oleh peningkatan permintaan untuk peranti perkakasan terbenam, beberapa reka bentuk IC dan langkah pembuatan telah disalurkan kepada vendor pihak ketiga. Memandangkan vendor pihak ketiga yang berniat jahat boleh memasukkan litar berniat jahat, dipanggil Trojan perkakasan, ke dalam produk mereka, membangunkan kaedah pengesanan perkakasan-Trojan yang berkesan amat diperlukan. Dalam makalah ini, kami mencadangkan 25 ciri perkakasan-Trojan yang memfokuskan pada struktur litar pencetus untuk pengesanan perkakasan-Trojan berasaskan pembelajaran mesin. Menggabungkan ciri yang dicadangkan ke dalam 11 ciri perkakasan-Trojan sedia ada, kami menggunakan sepenuhnya 36 ciri perkakasan-Trojan untuk pengelasan. Kemudian kami mengklasifikasikan jaring dalam senarai jaring yang tidak diketahui ke dalam satu set jaring biasa dan jaring Trojan berdasarkan pengelas hutan rawak. Keputusan eksperimen menunjukkan bahawa purata kadar positif benar (TPR) menjadi 64.2% dan purata kadar negatif benar (TNR) menjadi 100.0%. Mereka meningkatkan purata TPR sebanyak 14.8 mata sambil mengekalkan purata TNR berbanding kaedah terkini yang sedia ada. Khususnya, kaedah yang dicadangkan berjaya mengetahui jaring Trojan dalam beberapa litar penanda aras, yang tidak ditemui oleh kaedah sedia ada.
Tatsuki KURIHARA
Waseda University
Nozomu TOGAWA
Waseda University
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Salinan
Tatsuki KURIHARA, Nozomu TOGAWA, "Hardware-Trojan Detection Based on the Structural Features of Trojan Circuits Using Random Forests" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 7, pp. 1049-1060, July 2022, doi: 10.1587/transfun.2021EAP1091.
Abstract: Recently, with the spread of Internet of Things (IoT) devices, embedded hardware devices have been used in a variety of everyday electrical items. Due to the increased demand for embedded hardware devices, some of the IC design and manufacturing steps have been outsourced to third-party vendors. Since malicious third-party vendors may insert malicious circuits, called hardware Trojans, into their products, developing an effective hardware-Trojan detection method is strongly required. In this paper, we propose 25 hardware-Trojan features focusing on the structure of trigger circuits for machine-learning-based hardware-Trojan detection. Combining the proposed features into 11 existing hardware-Trojan features, we totally utilize 36 hardware-Trojan features for classification. Then we classify the nets in an unknown netlist into a set of normal nets and Trojan nets based on a random-forest classifier. The experimental results demonstrate that the average true positive rate (TPR) becomes 64.2% and the average true negative rate (TNR) becomes 100.0%. They improve the average TPR by 14.8 points while keeping the average TNR compared to existing state-of-the-art methods. In particular, the proposed method successfully finds out Trojan nets in several benchmark circuits, which are not found by the existing method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAP1091/_p
Salinan
@ARTICLE{e105-a_7_1049,
author={Tatsuki KURIHARA, Nozomu TOGAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Hardware-Trojan Detection Based on the Structural Features of Trojan Circuits Using Random Forests},
year={2022},
volume={E105-A},
number={7},
pages={1049-1060},
abstract={Recently, with the spread of Internet of Things (IoT) devices, embedded hardware devices have been used in a variety of everyday electrical items. Due to the increased demand for embedded hardware devices, some of the IC design and manufacturing steps have been outsourced to third-party vendors. Since malicious third-party vendors may insert malicious circuits, called hardware Trojans, into their products, developing an effective hardware-Trojan detection method is strongly required. In this paper, we propose 25 hardware-Trojan features focusing on the structure of trigger circuits for machine-learning-based hardware-Trojan detection. Combining the proposed features into 11 existing hardware-Trojan features, we totally utilize 36 hardware-Trojan features for classification. Then we classify the nets in an unknown netlist into a set of normal nets and Trojan nets based on a random-forest classifier. The experimental results demonstrate that the average true positive rate (TPR) becomes 64.2% and the average true negative rate (TNR) becomes 100.0%. They improve the average TPR by 14.8 points while keeping the average TNR compared to existing state-of-the-art methods. In particular, the proposed method successfully finds out Trojan nets in several benchmark circuits, which are not found by the existing method.},
keywords={},
doi={10.1587/transfun.2021EAP1091},
ISSN={1745-1337},
month={July},}
Salinan
TY - JOUR
TI - Hardware-Trojan Detection Based on the Structural Features of Trojan Circuits Using Random Forests
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1049
EP - 1060
AU - Tatsuki KURIHARA
AU - Nozomu TOGAWA
PY - 2022
DO - 10.1587/transfun.2021EAP1091
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2022
AB - Recently, with the spread of Internet of Things (IoT) devices, embedded hardware devices have been used in a variety of everyday electrical items. Due to the increased demand for embedded hardware devices, some of the IC design and manufacturing steps have been outsourced to third-party vendors. Since malicious third-party vendors may insert malicious circuits, called hardware Trojans, into their products, developing an effective hardware-Trojan detection method is strongly required. In this paper, we propose 25 hardware-Trojan features focusing on the structure of trigger circuits for machine-learning-based hardware-Trojan detection. Combining the proposed features into 11 existing hardware-Trojan features, we totally utilize 36 hardware-Trojan features for classification. Then we classify the nets in an unknown netlist into a set of normal nets and Trojan nets based on a random-forest classifier. The experimental results demonstrate that the average true positive rate (TPR) becomes 64.2% and the average true negative rate (TNR) becomes 100.0%. They improve the average TPR by 14.8 points while keeping the average TNR compared to existing state-of-the-art methods. In particular, the proposed method successfully finds out Trojan nets in several benchmark circuits, which are not found by the existing method.
ER -