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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Hardware Trojan Detection based on Testability Measures in Gate Level Netlists using Machine Learning

Author 1: Thejaswini P
Author 2: Anu H
Author 3: Aravind H S
Author 4: D Mahesh Kumar
Author 5: Syed Asif
Author 6: Thirumalesh B
Author 7: Pooja C A
Author 8: Pavan G R

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0131225

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.

  • Abstract and Keywords
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Abstract: Modern integrated circuit design manufacturing involves outsourcing intellectual property to third-party vendors to cut down on overall cost. Since there is a partial surrender of control, these third-party vendors may introduce malicious circuit commonly known as Hardware Trojan into the system in such a way that it goes undetected by the end-users’ default security measures. Therefore, to mitigate the threat of functionality change caused by the Trojan, a technique is proposed based on the testability measures in gate level netlists using Machine Learning. The proposed technique detects the presence of Trojan from the gate-level description of nodes using controllability and observability values. Various Machine Learning models are implemented to classify the nodes as Trojan infected and non-infected. The efficiency of linear discriminant analysis obtains an accuracy of 92.85 %, precision of 99.9 %, recall of 80%, and F1 score of 88.8% with a latency of around 0.9 ms.

Keywords: Hardware trojan; machine learning; controllability; observability; detection and mitigation

Thejaswini P, Anu H, Aravind H S, D Mahesh Kumar, Syed Asif, Thirumalesh B, Pooja C A and Pavan G R, “Hardware Trojan Detection based on Testability Measures in Gate Level Netlists using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131225

@article{P2022,
title = {Hardware Trojan Detection based on Testability Measures in Gate Level Netlists using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131225},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131225},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Thejaswini P and Anu H and Aravind H S and D Mahesh Kumar and Syed Asif and Thirumalesh B and Pooja C A and Pavan G R}
}


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