Abstract
Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 227-232 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538603512 |
| DOIs | |
| Publication status | Published - 2017 Sep 19 |
| Event | 23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 - Thessaloniki, Greece Duration: 2017 Jul 3 → 2017 Jul 5 |
Other
| Other | 23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 |
|---|---|
| Country | Greece |
| City | Thessaloniki |
| Period | 17/7/3 → 17/7/5 |
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Keywords
- Gate-level netlist
- Hardware Trojan
- Machine learning
- Multi-layer
- Neural network
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications
- Hardware and Architecture
- Control and Systems Engineering
Cite this
Hardware Trojans classification for gate-level netlists using multi-layer neural networks. / Hasegawa, Kento; Yanagisawa, Masao; Togawa, Nozomu.
2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 227-232 8046227.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Hardware Trojans classification for gate-level netlists using multi-layer neural networks
AU - Hasegawa, Kento
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
PY - 2017/9/19
Y1 - 2017/9/19
N2 - Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.
AB - Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.
KW - Gate-level netlist
KW - Hardware Trojan
KW - Machine learning
KW - Multi-layer
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=85032723104&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032723104&partnerID=8YFLogxK
U2 - 10.1109/IOLTS.2017.8046227
DO - 10.1109/IOLTS.2017.8046227
M3 - Conference contribution
AN - SCOPUS:85032723104
SP - 227
EP - 232
BT - 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -