A Hardware Trojan-Detection Technique Based on Suspicious Circuit Block Partition
To ensure that a hardware Trojan remains hidden in a circuit, it is usually necessary to ensure that the trigger signal has a low testability, which has been widely recognized and proven. The most advanced testability-based detection methods are rather slow for large circuits, and the false-positive...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/24/4138 |
| _version_ | 1797460083837239296 |
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| author | Jiajie Mao Xiaowen Jiang Dehong Liu Jianjun Chen Kai Huang |
| author_facet | Jiajie Mao Xiaowen Jiang Dehong Liu Jianjun Chen Kai Huang |
| author_sort | Jiajie Mao |
| collection | DOAJ |
| description | To ensure that a hardware Trojan remains hidden in a circuit, it is usually necessary to ensure that the trigger signal has a low testability, which has been widely recognized and proven. The most advanced testability-based detection methods are rather slow for large circuits, and the false-positive rate is not as low as that for small circuits. In this paper, a hardware Trojan, through the low testability of the trigger signal and its position characteristics in the circuit, was detected, which greatly improves the detection speed while maintaining a lower false positive rate when being applied to large circuits. First, the Sandia Controllability/Observability Analysis Program (SCOAP) was applied to obtain the 0–1 controllability of the signals in the netlist. Secondly, the controllability value was calculated by the differential amplification model, in order to facilitate K-means clustering to get better results. Then, we calculate the shortest path between each suspicious signal to get the connection between each suspicious signal. Finally, we divide the suspicious signals into several suspicious circuit blocks to screen the real trigger signal. As a result, the false-negative rate of 0% and the highest false-positive rate of 5.02% were obtained on the Trust-Hub benchmarks. |
| first_indexed | 2024-03-09T17:00:08Z |
| format | Article |
| id | doaj.art-f06613cbe2084fe99475f455057da494 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T17:00:08Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-f06613cbe2084fe99475f455057da4942023-11-24T14:30:57ZengMDPI AGElectronics2079-92922022-12-011124413810.3390/electronics11244138A Hardware Trojan-Detection Technique Based on Suspicious Circuit Block PartitionJiajie Mao0Xiaowen Jiang1Dehong Liu2Jianjun Chen3Kai Huang4College of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Micro-Nano Electronics, Zhejiang University, Hangzhou 310027, ChinaDigital Grid Research Institute, China Southern Power Grid, Guangzhou 510670, ChinaDigital Grid Research Institute, China Southern Power Grid, Guangzhou 510670, ChinaSchool of Micro-Nano Electronics, Zhejiang University, Hangzhou 310027, ChinaTo ensure that a hardware Trojan remains hidden in a circuit, it is usually necessary to ensure that the trigger signal has a low testability, which has been widely recognized and proven. The most advanced testability-based detection methods are rather slow for large circuits, and the false-positive rate is not as low as that for small circuits. In this paper, a hardware Trojan, through the low testability of the trigger signal and its position characteristics in the circuit, was detected, which greatly improves the detection speed while maintaining a lower false positive rate when being applied to large circuits. First, the Sandia Controllability/Observability Analysis Program (SCOAP) was applied to obtain the 0–1 controllability of the signals in the netlist. Secondly, the controllability value was calculated by the differential amplification model, in order to facilitate K-means clustering to get better results. Then, we calculate the shortest path between each suspicious signal to get the connection between each suspicious signal. Finally, we divide the suspicious signals into several suspicious circuit blocks to screen the real trigger signal. As a result, the false-negative rate of 0% and the highest false-positive rate of 5.02% were obtained on the Trust-Hub benchmarks.https://www.mdpi.com/2079-9292/11/24/4138hardware securityhardware Trojandifference-amplified controllabilityk-means clusteringlogical distancecircuit block partition |
| spellingShingle | Jiajie Mao Xiaowen Jiang Dehong Liu Jianjun Chen Kai Huang A Hardware Trojan-Detection Technique Based on Suspicious Circuit Block Partition Electronics hardware security hardware Trojan difference-amplified controllability k-means clustering logical distance circuit block partition |
| title | A Hardware Trojan-Detection Technique Based on Suspicious Circuit Block Partition |
| title_full | A Hardware Trojan-Detection Technique Based on Suspicious Circuit Block Partition |
| title_fullStr | A Hardware Trojan-Detection Technique Based on Suspicious Circuit Block Partition |
| title_full_unstemmed | A Hardware Trojan-Detection Technique Based on Suspicious Circuit Block Partition |
| title_short | A Hardware Trojan-Detection Technique Based on Suspicious Circuit Block Partition |
| title_sort | hardware trojan detection technique based on suspicious circuit block partition |
| topic | hardware security hardware Trojan difference-amplified controllability k-means clustering logical distance circuit block partition |
| url | https://www.mdpi.com/2079-9292/11/24/4138 |
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