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

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...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2022, Vol.13 (12)
Main Authors: P, Thejaswini, H, Anu, S, Aravind H, Kumar, D Mahesh, Asif, Syed, B, Thirumalesh, A, Pooja C, R, Pavan G
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Art techniques
Circuit design
Circuits
Computer engineering
Computer science
Design
Discriminant analysis
Hardware
Integrated circuits
Intellectual property
Machine learning
Malware
Manufacturing
Nodes
Observability (systems)
Principal components analysis
Software
Support vector machines
Testability
Third party
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Summary: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2022.0131225