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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Vydané v:	Measurement : journal of the International Measurement Confederation Ročník 194; s. 111055
Hlavní autori:	Priyatharishini, M., Nirmala Devi, M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Elsevier Ltd 15.05.2022 Elsevier Science Ltd
Predmet:	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
On-line prístup:	Získať plný text
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