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Empirical Evaluation and Optimization of Hardware-Trojan Classification for Gate-Level Netlists Based on Multi-Layer Neural Networks
Kento HASEGAWA Masao YANAGISAWA Nozomu TOGAWA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2018/12/01
Online ISSN: 1745-1337
Type of Manuscript: Special Section LETTER (Special Section on VLSI Design and CAD Algorithms)
hardware Trojan, detection, gate-level netlist, multi-layer neural networks, machine learning,
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Recently, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, malicious third-party vendors can easily insert hardware Trojans in their products and thus we have to detect them efficiently. 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. By experimentally optimizing the structure of multi-layer neural networks, we can obtain an average of 84.8% true positive rate and an average of 70.1% true negative rate while we can obtain 100% true positive rate in some of the benchmarks, which outperforms the existing methods in most of the cases.