Abstract
Over the past 10 years, various Hardware Trojan (HT) detection techniques have been proposed by the research community. However, the development of HT benchmark suites for testing and evaluating HT detection techniques lags behind. The number of HT-infected circuits available in current public HT benchmarks is somewhat limited and the circuits lack diversity in structure. Therefore, this paper proposes a new method to generate HTs using a highly configurable generation platform based on transition probability. The generation platform is highly configurable in terms of the HT trigger condition, trigger type, payload type and in the number and variety of HT-infected circuits that can be generated. In this research the transition probability of netlists is employed to identify rarely activated internal nodes to target for HT insertion rather than functional simulation as utilised in previous research. The authors believe transition probability provides a more realistic reflection of the netlist activity for use in determining the appropriate position for HT insertion. Finally, the generated HT-infected circuits are tested by a machine learning (ML)-based HT detection technique, which is known as Controllability and Observability for HT Detection (COTD). The resulting false positive and false negative rates illustrate the feasibility of the benchmark suite.
Original language | English |
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Title of host publication | IEEE Computer Society Annual Symposium on VLSI (ISVLSI) |
Publisher | IEEE |
Pages | 302-307 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 19 Sep 2019 |
Event | IEEE Computer Society Annual Symposium on VLSI 2019 - Miami, Florida, Miami, United States Duration: 15 Jul 2019 → 17 Jul 2019 Conference number: 2019 http://www.eng.ucy.ac.cy/theocharides/isvlsi19/dates.html |
Conference
Conference | IEEE Computer Society Annual Symposium on VLSI 2019 |
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Abbreviated title | ISVLSI |
Country/Territory | United States |
City | Miami |
Period | 15/07/2019 → 17/07/2019 |
Internet address |
Keywords
- Hardware Trojans
- Benchmarks
- Automatic Generation
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Fingerprint
Dive into the research topics of 'An Improved Automatic Hardware Trojan Generation Platform'. Together they form a unique fingerprint.Student Theses
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Automated hardware trojan detection
Author: Yu, S., Dec 2021Supervisor: O'Neill, M. (Supervisor), Gu, C. (Supervisor) & Liu, W. (External person) (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy
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