Abstract
Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as 'hardware Trojans') into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.
Original language | English |
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Title of host publication | 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 |
Publisher | IEEE Computer Society |
Volume | 2018-September |
ISBN (Electronic) | 9781538660959 |
DOIs | |
Publication status | Published - 2018 Dec 13 |
Event | 8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 - Berlin, Germany Duration: 2018 Sep 2 → 2018 Sep 5 |
Other
Other | 8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 |
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Country | Germany |
City | Berlin |
Period | 18/9/2 → 18/9/5 |
Keywords
- design time
- gate-level netlist
- hardware Trojan
- machine learning
- neural network
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
- Media Technology