Hardware Trojan Detection Utilizing Machine Learning Approaches

Kento Hasegawa; Youhua Shi; Nozomu Togawa

doi:10.1109/TrustCom/BigDataSE.2018.00287

Hardware Trojan Detection Utilizing Machine Learning Approaches

Kento Hasegawa, Youhua Shi, Nozomu Togawa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

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	https://doi.org/10.1109/TrustCom/BigDataSE.2018.00287
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

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

Access to Document

10.1109/TrustCom/BigDataSE.2018.00287

Fingerprint Dive into the research topics of 'Hardware Trojan Detection Utilizing Machine Learning Approaches'. Together they form a unique fingerprint.

Cite this

Hasegawa, K., Shi, Y., & Togawa, N. (2018). Hardware Trojan Detection Utilizing Machine Learning Approaches. In 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 (pp. 1891-1896). [8456155] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00287

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

Hasegawa, K, Shi, Y & Togawa, N 2018, Hardware Trojan Detection Utilizing Machine Learning Approaches. in 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., 8456155, Institute of Electrical and Electronics Engineers Inc., pp. 1891-1896, 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, 18/7/31. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00287

Hasegawa K, Shi Y , Togawa N. Hardware Trojan Detection Utilizing Machine Learning Approaches. In 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 https://doi.org/10.1109/TrustCom/BigDataSE.2018.00287

Hasegawa, Kento ; Shi, Youhua ; Togawa, Nozomu. / Hardware Trojan Detection Utilizing Machine Learning Approaches. 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. pp. 1891-1896

@inproceedings{9dcd2f2def82461783e4d29564df0491,

title = "Hardware Trojan Detection Utilizing Machine Learning Approaches",

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.",

keywords = "design time, hardware security, hardware Trojan, IoT, machine learning",

author = "Kento Hasegawa and Youhua Shi and Nozomu Togawa",

year = "2018",

month = sep,

day = "5",

doi = "10.1109/TrustCom/BigDataSE.2018.00287",

language = "English",

isbn = "9781538643877",

pages = "1891--1896",

booktitle = "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.",

note = "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 ; Conference date: 31-07-2018 Through 03-08-2018",

}

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.

T2 - 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

Y2 - 31 July 2018 through 3 August 2018

ER -

Hardware Trojan Detection Utilizing Machine Learning Approaches

Abstract

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this