Implementing Region-Based Segmentation for Hardware Trojan Detection in FPGAs Cell-Level Netlist
Summary :
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%.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.11 pp.1926-1929
- Publication Date
- 2023/11/01
- Publicized
- 2023/07/28
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023EDL8036
- Type of Manuscript
- LETTER
- Category
- Dependable Computing
Authors
Ann Jelyn TIEMPO
Kwangwoon University
Yong-Jin JEONG
Kwangwoon University
Keyword
Latest Issue
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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
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@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},}
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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 -
English
