A deep learning based malicious module identification using stacked sparse autoencoder network for VLSI circuit reliability

•A deep learning- based hardware trojan identification method is proposed.•The data imbalance in VLSI circuits is addressed by using deep convolutional generative adversarial network.•More suitable features are extracted using handcrafted algorithms and structural reports.•Stacked autoencoder and st...

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Veröffentlicht in:	Measurement : journal of the International Measurement Confederation Jg. 194; S. 111055
Hauptverfasser:	Priyatharishini, M., Nirmala Devi, M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Elsevier Ltd 15.05.2022 Elsevier Science Ltd
Schlagworte:	Circuit design Circuit reliability Deep learning Feature Feature extraction Generative Adversarial Network Hardware Trojan Identification methods Integrated circuits Learning Modules Network reliability Neural networks Optimization Performance degradation Sparsity Stacked Sparse Autoencoder Threat models Very large scale integration Generative Adversarial Network Feature Hardware Trojan Stacked Sparse Autoencoder
ISSN:	0263-2241, 1873-412X
Online-Zugang:	Volltext
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