Abstract
Hardware security has become a serious concern in recent years. Due to the outsourcing in hardware production, malicious circuits (or hardware Trojans) can be easily inserted into hardware products by attackers. Since hardware Trojans are tiny and stealthy, their detection is difficult. Under the circumstances, numerous hardware-Trojan detection methods have been proposed. In this paper, we elaborate the overview of hardware-Trojan detection and review the hardware-Trojan detection methods using machine learning which is one of the state-of-the-art approaches.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1891-1896 |
| Number of pages | 6 |
| ISBN (Print) | 9781538643877 |
| DOIs | |
| Publication status | Published - 2018 Sep 5 |
| Event | 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 - New York, United States Duration: 2018 Jul 31 → 2018 Aug 3 |
Other
| Other | 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018 |
|---|---|
| Country | United States |
| City | New York |
| Period | 18/7/31 → 18/8/3 |
Fingerprint
Keywords
- design time
- hardware security
- hardware Trojan
- IoT
- machine learning
ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture
- Information Systems
- Information Systems and Management
- Safety, Risk, Reliability and Quality
Cite this
Hardware Trojan Detection Utilizing Machine Learning Approaches. / Hasegawa, Kento; Shi, Youhua; Togawa, Nozomu.
Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1891-1896 8456155.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Hardware Trojan Detection Utilizing Machine Learning Approaches
AU - Hasegawa, Kento
AU - Shi, Youhua
AU - Togawa, Nozomu
PY - 2018/9/5
Y1 - 2018/9/5
N2 - Hardware security has become a serious concern in recent years. Due to the outsourcing in hardware production, malicious circuits (or hardware Trojans) can be easily inserted into hardware products by attackers. Since hardware Trojans are tiny and stealthy, their detection is difficult. Under the circumstances, numerous hardware-Trojan detection methods have been proposed. In this paper, we elaborate the overview of hardware-Trojan detection and review the hardware-Trojan detection methods using machine learning which is one of the state-of-the-art approaches.
AB - Hardware security has become a serious concern in recent years. Due to the outsourcing in hardware production, malicious circuits (or hardware Trojans) can be easily inserted into hardware products by attackers. Since hardware Trojans are tiny and stealthy, their detection is difficult. Under the circumstances, numerous hardware-Trojan detection methods have been proposed. In this paper, we elaborate the overview of hardware-Trojan detection and review the hardware-Trojan detection methods using machine learning which is one of the state-of-the-art approaches.
KW - design time
KW - hardware security
KW - hardware Trojan
KW - IoT
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85054094019&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054094019&partnerID=8YFLogxK
U2 - 10.1109/TrustCom/BigDataSE.2018.00287
DO - 10.1109/TrustCom/BigDataSE.2018.00287
M3 - Conference contribution
AN - SCOPUS:85054094019
SN - 9781538643877
SP - 1891
EP - 1896
BT - Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -