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
A segurança cibernética tornou-se uma preocupação séria em nossas vidas diárias. As funções maliciosas inseridas em dispositivos de hardware são conhecidas como trojans de hardware. Nesta carta, propomos um método de classificação de Trojan de hardware em netlists de nível de portão utilizando estruturas de rede de limite. Primeiro usamos um método de detecção de Trojan de hardware baseado em aprendizado de máquina e classificamos as redes em uma determinada netlist em um conjunto de redes normais e um conjunto de redes de Trojan. Com base nos resultados da classificação, investigamos as estruturas de rede em torno da fronteira entre redes normais e redes de Trojan e extraímos as características das redes erroneamente identificadas como redes normais ou redes de Trojan. Finalmente, com base nas características extraídas das redes de fronteira, classificamos novamente as redes em uma determinada netlist em um conjunto de redes normais e um conjunto de redes Trojan. Os resultados experimentais demonstram que nosso método proposto supera um método de detecção de Trojan de hardware baseado em aprendizado de máquina existente em termos de sua verdadeira taxa positiva.
Kento HASEGAWA
Waseda University
Masao YANAGISAWA
Waseda University
Nozomu TOGAWA
Waseda University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copiar
Kento HASEGAWA, Masao YANAGISAWA, Nozomu TOGAWA, "Trojan-Net Classification for Gate-Level Hardware Design Utilizing Boundary Net Structures" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 7, pp. 1618-1622, July 2020, doi: 10.1587/transinf.2019ICL0003.
Abstract: Cybersecurity has become a serious concern in our daily lives. The malicious functions inserted into hardware devices have been well known as hardware Trojans. In this letter, we propose a hardware-Trojan classification method at gate-level netlists utilizing boundary net structures. We first use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. Based on the classification results, we investigate the net structures around the boundary between normal nets and Trojan nets, and extract the features of the nets mistakenly identified to be normal nets or Trojan nets. Finally, based on the extracted features of the boundary nets, we again classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. The experimental results demonstrate that our proposed method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of its true positive rate.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019ICL0003/_p
Copiar
@ARTICLE{e103-d_7_1618,
author={Kento HASEGAWA, Masao YANAGISAWA, Nozomu TOGAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Trojan-Net Classification for Gate-Level Hardware Design Utilizing Boundary Net Structures},
year={2020},
volume={E103-D},
number={7},
pages={1618-1622},
abstract={Cybersecurity has become a serious concern in our daily lives. The malicious functions inserted into hardware devices have been well known as hardware Trojans. In this letter, we propose a hardware-Trojan classification method at gate-level netlists utilizing boundary net structures. We first use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. Based on the classification results, we investigate the net structures around the boundary between normal nets and Trojan nets, and extract the features of the nets mistakenly identified to be normal nets or Trojan nets. Finally, based on the extracted features of the boundary nets, we again classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. The experimental results demonstrate that our proposed method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of its true positive rate.},
keywords={},
doi={10.1587/transinf.2019ICL0003},
ISSN={1745-1361},
month={July},}
Copiar
TY - JOUR
TI - Trojan-Net Classification for Gate-Level Hardware Design Utilizing Boundary Net Structures
T2 - IEICE TRANSACTIONS on Information
SP - 1618
EP - 1622
AU - Kento HASEGAWA
AU - Masao YANAGISAWA
AU - Nozomu TOGAWA
PY - 2020
DO - 10.1587/transinf.2019ICL0003
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2020
AB - Cybersecurity has become a serious concern in our daily lives. The malicious functions inserted into hardware devices have been well known as hardware Trojans. In this letter, we propose a hardware-Trojan classification method at gate-level netlists utilizing boundary net structures. We first use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. Based on the classification results, we investigate the net structures around the boundary between normal nets and Trojan nets, and extract the features of the nets mistakenly identified to be normal nets or Trojan nets. Finally, based on the extracted features of the boundary nets, we again classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. The experimental results demonstrate that our proposed method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of its true positive rate.
ER -