Abstract
Recently, due to the increase of outsourcing in IC design and manufacturing, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, it is strongly required to detect hardware Trojans because malicious third-party vendors can easily insert hardware Trojans in their products. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. We obtained at most 100% true positive rate with our proposed method.
Original language | English |
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Title of host publication | 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 227-232 |
Number of pages | 6 |
ISBN (Electronic) | 9781538603512 |
DOIs | |
Publication status | Published - 2017 Sep 19 |
Event | 23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 - Thessaloniki, Greece Duration: 2017 Jul 3 → 2017 Jul 5 |
Other
Other | 23rd IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2017 |
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Country | Greece |
City | Thessaloniki |
Period | 17/7/3 → 17/7/5 |
Keywords
- Gate-level netlist
- Hardware Trojan
- Machine learning
- Multi-layer
- Neural network
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications
- Hardware and Architecture
- Control and Systems Engineering