抄録
Since hardware production become inexpensive and international, hardware vendors often outsource their products to third-party vendors. Due to the situation, malicious vendors can easily insert malfunctions (also known as 'hardware Trojans') to their products. In this paper, we experimentally evaluate a machine-learning-based hardware-Trojan detection method using several hardware Trojans we designed. To begin with, we design three types of hardware Trojans and insert them to simple RS232 transceiver circuits. After that, we learn known netlists, where we know which nets are Trojan ones or normal ones beforehand, using a machine-learning-based hardware-Trojan detection method with a support vector machine (SVM) classifier. Finally, we classify the nets in the designed hardware-Trojan-inserted netlists into a set of Trojan nets and that of normal nets using the learned classifier. The experimental results demonstrate that the hardware-Trojan detection method with the SVM-based approach can detect a part of hardware Trojans we designed.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017 |
| 出版者 | IEEE Computer Society |
| ページ | 811-814 |
| ページ数 | 4 |
| 巻 | 2017-October |
| ISBN（電子版） | 9781509066247 |
| DOI | |
| 出版物ステータス | Published - 2018 1 8 |
| イベント | 12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017 - Guiyang, China 継続期間: 2017 10 25 → 2017 10 28 |
Other
| Other | 12th IEEE International Conference on Advanced Semiconductor Integrated Circuits, ASICON 2017 |
|---|---|
| 国 | China |
| 市 | Guiyang |
| 期間 | 17/10/25 → 17/10/28 |
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ASJC Scopus subject areas
- Hardware and Architecture
- Electrical and Electronic Engineering
これを引用
Designing hardware trojans and their detection based on a SVM-based approach. / Inoue, Tomotaka; Hasegawa, Kento; Yanagisawa, Masao; Togawa, Nozomu.
Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017. 巻 2017-October IEEE Computer Society, 2018. p. 811-814.研究成果: Conference contribution
}
TY - GEN
T1 - Designing hardware trojans and their detection based on a SVM-based approach
AU - Inoue, Tomotaka
AU - Hasegawa, Kento
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
PY - 2018/1/8
Y1 - 2018/1/8
N2 - Since hardware production become inexpensive and international, hardware vendors often outsource their products to third-party vendors. Due to the situation, malicious vendors can easily insert malfunctions (also known as 'hardware Trojans') to their products. In this paper, we experimentally evaluate a machine-learning-based hardware-Trojan detection method using several hardware Trojans we designed. To begin with, we design three types of hardware Trojans and insert them to simple RS232 transceiver circuits. After that, we learn known netlists, where we know which nets are Trojan ones or normal ones beforehand, using a machine-learning-based hardware-Trojan detection method with a support vector machine (SVM) classifier. Finally, we classify the nets in the designed hardware-Trojan-inserted netlists into a set of Trojan nets and that of normal nets using the learned classifier. The experimental results demonstrate that the hardware-Trojan detection method with the SVM-based approach can detect a part of hardware Trojans we designed.
AB - Since hardware production become inexpensive and international, hardware vendors often outsource their products to third-party vendors. Due to the situation, malicious vendors can easily insert malfunctions (also known as 'hardware Trojans') to their products. In this paper, we experimentally evaluate a machine-learning-based hardware-Trojan detection method using several hardware Trojans we designed. To begin with, we design three types of hardware Trojans and insert them to simple RS232 transceiver circuits. After that, we learn known netlists, where we know which nets are Trojan ones or normal ones beforehand, using a machine-learning-based hardware-Trojan detection method with a support vector machine (SVM) classifier. Finally, we classify the nets in the designed hardware-Trojan-inserted netlists into a set of Trojan nets and that of normal nets using the learned classifier. The experimental results demonstrate that the hardware-Trojan detection method with the SVM-based approach can detect a part of hardware Trojans we designed.
KW - Design time
KW - Gate-level netlist
KW - Hardware Trojan
KW - Machine learning
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85044751081&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044751081&partnerID=8YFLogxK
U2 - 10.1109/ASICON.2017.8252600
DO - 10.1109/ASICON.2017.8252600
M3 - Conference contribution
AN - SCOPUS:85044751081
VL - 2017-October
SP - 811
EP - 814
BT - Proceedings - 2017 IEEE 12th International Conference on ASIC, ASICON 2017
PB - IEEE Computer Society
ER -