Empirical evaluation and optimization of hardware-Trojan classification for gate-level netlists based on multi-layer neural networks

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2320-2326
Number of pages7
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE101A
Issue number12
DOIs
Publication statusPublished - 2018 Dec

Keywords

  • Detection
  • Gate-level netlist
  • Hardware Trojan
  • Machine learning
  • Multi-layer neural networks

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Empirical evaluation and optimization of hardware-Trojan classification for gate-level netlists based on multi-layer neural networks'. Together they form a unique fingerprint.

Cite this