IEICE TRANSACTIONS on Fundamentals
Hardware-Trojan Detection Based on the Structural Features of Trojan Circuits Using Random Forests
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.7 pp.1049-1060
- Publication Date
- 2022/07/01
- Publicized
- 2022/01/07
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2021EAP1091
- Type of Manuscript
- PAPER
- Category
- VLSI Design Technology and CAD
Authors
Tatsuki KURIHARA
Waseda University
Nozomu TOGAWA
Waseda University
Keyword
Latest Issue
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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
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@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},}
Copy
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 -
English
