SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier
Abstract Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we pr...
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| Autor*in: |
Tebyanian, Mahshid [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of electronic testing - Springer US, 1990, 37(2021), 4 vom: Aug., Seite 473-487 |
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| Übergeordnetes Werk: |
volume:37 ; year:2021 ; number:4 ; month:08 ; pages:473-487 |
| Links: |
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| DOI / URN: |
10.1007/s10836-021-05960-2 |
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| 520 | |a Abstract Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches. | ||
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10.1007/s10836-021-05960-2 doi (DE-627)OLC2077423587 (DE-He213)s10836-021-05960-2-p DE-627 ger DE-627 rakwb eng 004 670 VZ Tebyanian, Mahshid verfasserin aut SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches. Controllability and observability Hardware testability K -means clustering Hardware Trojan detection Hardware security Mokhtarpour, Azadeh aut Shafieinejad, Alireza (orcid)0000-0002-2449-2914 aut Enthalten in Journal of electronic testing Springer US, 1990 37(2021), 4 vom: Aug., Seite 473-487 (DE-627)130869090 (DE-600)1033317-4 (DE-576)024991600 0923-8174 nnns volume:37 year:2021 number:4 month:08 pages:473-487 https://doi.org/10.1007/s10836-021-05960-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 37 2021 4 08 473-487 |
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10.1007/s10836-021-05960-2 doi (DE-627)OLC2077423587 (DE-He213)s10836-021-05960-2-p DE-627 ger DE-627 rakwb eng 004 670 VZ Tebyanian, Mahshid verfasserin aut SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches. Controllability and observability Hardware testability K -means clustering Hardware Trojan detection Hardware security Mokhtarpour, Azadeh aut Shafieinejad, Alireza (orcid)0000-0002-2449-2914 aut Enthalten in Journal of electronic testing Springer US, 1990 37(2021), 4 vom: Aug., Seite 473-487 (DE-627)130869090 (DE-600)1033317-4 (DE-576)024991600 0923-8174 nnns volume:37 year:2021 number:4 month:08 pages:473-487 https://doi.org/10.1007/s10836-021-05960-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 37 2021 4 08 473-487 |
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10.1007/s10836-021-05960-2 doi (DE-627)OLC2077423587 (DE-He213)s10836-021-05960-2-p DE-627 ger DE-627 rakwb eng 004 670 VZ Tebyanian, Mahshid verfasserin aut SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches. Controllability and observability Hardware testability K -means clustering Hardware Trojan detection Hardware security Mokhtarpour, Azadeh aut Shafieinejad, Alireza (orcid)0000-0002-2449-2914 aut Enthalten in Journal of electronic testing Springer US, 1990 37(2021), 4 vom: Aug., Seite 473-487 (DE-627)130869090 (DE-600)1033317-4 (DE-576)024991600 0923-8174 nnns volume:37 year:2021 number:4 month:08 pages:473-487 https://doi.org/10.1007/s10836-021-05960-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 37 2021 4 08 473-487 |
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10.1007/s10836-021-05960-2 doi (DE-627)OLC2077423587 (DE-He213)s10836-021-05960-2-p DE-627 ger DE-627 rakwb eng 004 670 VZ Tebyanian, Mahshid verfasserin aut SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches. Controllability and observability Hardware testability K -means clustering Hardware Trojan detection Hardware security Mokhtarpour, Azadeh aut Shafieinejad, Alireza (orcid)0000-0002-2449-2914 aut Enthalten in Journal of electronic testing Springer US, 1990 37(2021), 4 vom: Aug., Seite 473-487 (DE-627)130869090 (DE-600)1033317-4 (DE-576)024991600 0923-8174 nnns volume:37 year:2021 number:4 month:08 pages:473-487 https://doi.org/10.1007/s10836-021-05960-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 37 2021 4 08 473-487 |
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sc-cotd: hardware trojan detection based on sequential/combinational testability features using ensemble classifier |
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SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier |
| abstract |
Abstract Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Security against Hardware Trojans (HT) is an important concern in integrated circuits (IC) design and fabrication. Most of the current HT detection methods are based on the golden model of circuit design. Further, some approaches require test pattern for HTs activation. In this paper, we propose SC-COTD (Sequential/Combinational Controllability and Observability features for hardware Trojan Detection), an effective hardware Trojan detection to get rid of both golden chip and test pattern limitations. SC-COTD uses both sequential and combinational testability measures to detect and locate HT signals by a machine learning approach. This method deploys an ensemble classifier based on k-means clustering. The clustering models have diverse variety in testability features along with size of clustering which inspect and reveal different aspects of netlist conventional for a collaborative scheme. The clustering results are filtered and then fed into a decision-making procedure based on majority voting to eliminate the limited flaws of each model. The evaluation results on TrustHUB benchmarks demonstrate that, SC-COTD can detect and locate HTs with 100% without any false negative, i.e., Recall = 1. Although our method has a limited number of false positive, it has the best performance in comparison to well-known previous approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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SC-COTD: Hardware Trojan Detection Based on Sequential/Combinational Testability Features using Ensemble Classifier |
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https://doi.org/10.1007/s10836-021-05960-2 |
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Mokhtarpour, Azadeh Shafieinejad, Alireza |
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Mokhtarpour, Azadeh Shafieinejad, Alireza |
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10.1007/s10836-021-05960-2 |
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2024-07-03T15:29:01.637Z |
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