Abstract
Recently, cybersecurity has become a serious concern for us. For example, the threats of hardware Trojans (malfunctions inserted into hardware devices) have appeared. Since hardware vendors often outsource parts of their hardware products to third-party vendors, the risk of hardware-Trojan insertion has been increased. Especially in the hardware design step, malicious vendors have a chance to insert hardware Trojans easily. In this paper, we propose a hardware-Trojan classification method utilizing boundary net structures. To begin with, we use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and that of Trojan nets. Based on the classification, we investigate the nets around the boundary between normal nets and Trojan nets and extract the features of the nets identified to be normal nets or Trojan nets mistakenly. Finally, using the classification results of machine-learning-based hardware-Trojan detection and the extracted features of the boundary nets, we classify the nets in a given netlist into a set of normal nets and that of Trojan nets again. The experimental results demonstrate that our method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of true positive rate.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-4 |
| Number of pages | 4 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538630259 |
| DOIs | |
| Publication status | Published - 2018 Mar 26 |
| Event | 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 - Las Vegas, United States Duration: 2018 Jan 12 → 2018 Jan 14 |
Other
| Other | 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 18/1/12 → 18/1/14 |
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Keywords
- boundary nets
- gate-level netlist
- hardware design
- hardware Trojan
- Trojan feature
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering
- Media Technology
Cite this
A hardware-Trojan classification method utilizing boundary net structures. / Hasegawa, Kento; Yanagisawa, Masao; Togawa, Nozomu.
2018 IEEE International Conference on Consumer Electronics, ICCE 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A hardware-Trojan classification method utilizing boundary net structures
AU - Hasegawa, Kento
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
PY - 2018/3/26
Y1 - 2018/3/26
N2 - Recently, cybersecurity has become a serious concern for us. For example, the threats of hardware Trojans (malfunctions inserted into hardware devices) have appeared. Since hardware vendors often outsource parts of their hardware products to third-party vendors, the risk of hardware-Trojan insertion has been increased. Especially in the hardware design step, malicious vendors have a chance to insert hardware Trojans easily. In this paper, we propose a hardware-Trojan classification method utilizing boundary net structures. To begin with, we use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and that of Trojan nets. Based on the classification, we investigate the nets around the boundary between normal nets and Trojan nets and extract the features of the nets identified to be normal nets or Trojan nets mistakenly. Finally, using the classification results of machine-learning-based hardware-Trojan detection and the extracted features of the boundary nets, we classify the nets in a given netlist into a set of normal nets and that of Trojan nets again. The experimental results demonstrate that our method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of true positive rate.
AB - Recently, cybersecurity has become a serious concern for us. For example, the threats of hardware Trojans (malfunctions inserted into hardware devices) have appeared. Since hardware vendors often outsource parts of their hardware products to third-party vendors, the risk of hardware-Trojan insertion has been increased. Especially in the hardware design step, malicious vendors have a chance to insert hardware Trojans easily. In this paper, we propose a hardware-Trojan classification method utilizing boundary net structures. To begin with, we use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and that of Trojan nets. Based on the classification, we investigate the nets around the boundary between normal nets and Trojan nets and extract the features of the nets identified to be normal nets or Trojan nets mistakenly. Finally, using the classification results of machine-learning-based hardware-Trojan detection and the extracted features of the boundary nets, we classify the nets in a given netlist into a set of normal nets and that of Trojan nets again. The experimental results demonstrate that our method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of true positive rate.
KW - boundary nets
KW - gate-level netlist
KW - hardware design
KW - hardware Trojan
KW - Trojan feature
UR - http://www.scopus.com/inward/record.url?scp=85048750003&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048750003&partnerID=8YFLogxK
U2 - 10.1109/ICCE.2018.8326247
DO - 10.1109/ICCE.2018.8326247
M3 - Conference contribution
VL - 2018-January
SP - 1
EP - 4
BT - 2018 IEEE International Conference on Consumer Electronics, ICCE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -