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 |
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| Sprache: | Englisch |
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| Abstract | •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 stacked sparse autoencoder model is used to discover the malicious logic in the gate level netlist.•The significance of sparsity is achieved by stacked sparse autoencoder technique, which minimizes the detection probability.
The rate of development of technology and its associated security issues are increasing in integrated circuit industry. This provides a space for implanting dormant nature of threats, named Hardware Trojan (HT) in various process of integrated circuits (IC) design. The impact of HT’s emanates the encrypted signals, privacy disruption, performance degradation or denial of service. The threat models are unimaginable and it can be intruded at any stage which complicates the HT detection process. Therefore, a deep learning-based malicious module identification method is proposed in this work by implementing stacked autoencoder and stacked sparse autoencoder model. The simulation results shows that the proposed stacked sparse autoencoder outperforms the best in detecting the malicious modifications with an average accuracy of 97.53%, true positive rate of 93% and moreover the true negative rate achieved is 98.14% which proves the effectiveness of sparsity nature in extracting suitable features in the proposed schemes. |
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| AbstractList | The rate of development of technology and its associated security issues are increasing in integrated circuit industry. This provides a space for implanting dormant nature of threats, named Hardware Trojan (HT) in various process of integrated circuits (IC) design. The impact of HT's emanates the encrypted signals, privacy disruption, performance degradation or denial of service. The threat models are unimaginable and it can be intruded at any stage which complicates the HT detection process. Therefore, a deep learning-based malicious module identification method is proposed in this work by implementing stacked autoencoder and stacked sparse autoencoder model. The simulation results shows that the proposed stacked sparse autoencoder outperforms the best in detecting the malicious modifications with an average accuracy of 97.53%, true positive rate of 93% and moreover the true negative rate achieved is 98.14% which proves the effectiveness of sparsity nature in extracting suitable features in the proposed schemes. •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 stacked sparse autoencoder model is used to discover the malicious logic in the gate level netlist.•The significance of sparsity is achieved by stacked sparse autoencoder technique, which minimizes the detection probability. The rate of development of technology and its associated security issues are increasing in integrated circuit industry. This provides a space for implanting dormant nature of threats, named Hardware Trojan (HT) in various process of integrated circuits (IC) design. The impact of HT’s emanates the encrypted signals, privacy disruption, performance degradation or denial of service. The threat models are unimaginable and it can be intruded at any stage which complicates the HT detection process. Therefore, a deep learning-based malicious module identification method is proposed in this work by implementing stacked autoencoder and stacked sparse autoencoder model. The simulation results shows that the proposed stacked sparse autoencoder outperforms the best in detecting the malicious modifications with an average accuracy of 97.53%, true positive rate of 93% and moreover the true negative rate achieved is 98.14% which proves the effectiveness of sparsity nature in extracting suitable features in the proposed schemes. |
| ArticleNumber | 111055 |
| Author | Priyatharishini, M. Nirmala Devi, M. |
| Author_xml | – sequence: 1 givenname: M. surname: Priyatharishini fullname: Priyatharishini, M. email: mpriyatharishinicb18@gmail.com – sequence: 2 givenname: M. surname: Nirmala Devi fullname: Nirmala Devi, M. email: m_nirmala@cb.amrita.edu |
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| CitedBy_id | crossref_primary_10_1016_j_measurement_2023_113320 crossref_primary_10_1109_TVLSI_2024_3458892 crossref_primary_10_3390_aerospace10090789 crossref_primary_10_1016_j_eswa_2023_121757 crossref_primary_10_1007_s11227_025_07357_w crossref_primary_10_1109_ACCESS_2022_3209705 crossref_primary_10_1016_j_aeue_2025_155959 |
| Cites_doi | 10.1109/DTIS.2016.7483804 10.3390/s16060895 10.1007/s10836-011-5255-2 10.1109/ACCESS.2020.3001239 10.1109/TSMC.2016.2605132 10.1109/IOLTS.2016.7604700 10.1007/s10836-018-5726-9 10.1155/2018/8676387 10.1109/JPROC.2014.2334493 10.1109/ACCESS.2018.2890693 10.1109/TVLSI.2009.2029117 10.1109/TCAD.2015.2488495 10.1109/MDT.2012.2196252 10.1080/00051144.2019.1631568 10.1109/TIFS.2016.2613842 10.1162/neco.2006.18.7.1527 10.1109/ICCCNT.2014.6963036 10.7873/DATE.2015.1101 10.1109/TCAD.2015.2422836 10.1109/TIFS.2011.2136339 10.23919/DATE51398.2021.9474076 10.1007/978-981-15-7234-0_30 10.1177/1550147719888098 10.1109/ACCESS.2019.2923799 10.1007/s10836-016-5631-z 10.1109/ASAP.2016.7760768 10.1109/THS.2015.7225334 10.1109/ISCAS.2017.8050827 |
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| Snippet | •A deep learning- based hardware trojan identification method is proposed.•The data imbalance in VLSI circuits is addressed by using deep convolutional... The rate of development of technology and its associated security issues are increasing in integrated circuit industry. This provides a space for implanting... |
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| SubjectTerms | 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 |
| Title | A deep learning based malicious module identification using stacked sparse autoencoder network for VLSI circuit reliability |
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