The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
FPGA(Field Programmable Gate Array)는 하드웨어 트로이 목마 삽입과 같은 보안 문제를 야기하는 재구성 가능성으로 인해 인기를 얻고 있습니다. 이러한 위협을 극복하기 위한 다양한 탐지 방법이 제안되었지만 ASIC의 공급망에서는 FPGA 애플리케이션에 직접 적용할 수 없습니다. 본 논문에서 저자는 아직 잘 연구되지 않은 셀 수준 넷리스트에서 하드웨어 트로이 목마를 탐지하기 위한 구조적 특징 기반 탐지 방법을 구현하는 것을 목표로 합니다. 구조적 유사성을 살펴봅니다. 실험은 평균 탐지율 95.41%, 평균 오경보율 2.87%, 평균 정확도 96.27%로 긍정적인 성능을 보여줍니다.
Ann Jelyn TIEMPO
Kwangwoon University
Yong-Jin JEONG
Kwangwoon University
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부
Ann Jelyn TIEMPO, Yong-Jin JEONG, "Implementing Region-Based Segmentation for Hardware Trojan Detection in FPGAs Cell-Level Netlist" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 11, pp. 1926-1929, November 2023, doi: 10.1587/transinf.2023EDL8036.
Abstract: Field Programmable Gate Array (FPGA) is gaining popularity because of their reconfigurability which brings in security concerns like inserting hardware trojan. Various detection methods to overcome this threat have been proposed but in the ASIC's supply chain and cannot directly apply to the FPGA application. In this paper, the authors aim to implement a structural feature-based detection method for detecting hardware trojan in a cell-level netlist, which is not well explored yet, where the nets are segmented into smaller groups based on their interconnection and further analyzed by looking at their structural similarities. Experiments show positive performance with an average detection rate of 95.41%, an average false alarm rate of 2.87% and average accuracy of 96.27%.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8036/_p
부
@ARTICLE{e106-d_11_1926,
author={Ann Jelyn TIEMPO, Yong-Jin JEONG, },
journal={IEICE TRANSACTIONS on Information},
title={Implementing Region-Based Segmentation for Hardware Trojan Detection in FPGAs Cell-Level Netlist},
year={2023},
volume={E106-D},
number={11},
pages={1926-1929},
abstract={Field Programmable Gate Array (FPGA) is gaining popularity because of their reconfigurability which brings in security concerns like inserting hardware trojan. Various detection methods to overcome this threat have been proposed but in the ASIC's supply chain and cannot directly apply to the FPGA application. In this paper, the authors aim to implement a structural feature-based detection method for detecting hardware trojan in a cell-level netlist, which is not well explored yet, where the nets are segmented into smaller groups based on their interconnection and further analyzed by looking at their structural similarities. Experiments show positive performance with an average detection rate of 95.41%, an average false alarm rate of 2.87% and average accuracy of 96.27%.},
keywords={},
doi={10.1587/transinf.2023EDL8036},
ISSN={1745-1361},
month={November},}
부
TY - JOUR
TI - Implementing Region-Based Segmentation for Hardware Trojan Detection in FPGAs Cell-Level Netlist
T2 - IEICE TRANSACTIONS on Information
SP - 1926
EP - 1929
AU - Ann Jelyn TIEMPO
AU - Yong-Jin JEONG
PY - 2023
DO - 10.1587/transinf.2023EDL8036
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2023
AB - Field Programmable Gate Array (FPGA) is gaining popularity because of their reconfigurability which brings in security concerns like inserting hardware trojan. Various detection methods to overcome this threat have been proposed but in the ASIC's supply chain and cannot directly apply to the FPGA application. In this paper, the authors aim to implement a structural feature-based detection method for detecting hardware trojan in a cell-level netlist, which is not well explored yet, where the nets are segmented into smaller groups based on their interconnection and further analyzed by looking at their structural similarities. Experiments show positive performance with an average detection rate of 95.41%, an average false alarm rate of 2.87% and average accuracy of 96.27%.
ER -