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A dual-stage deep learning approach for robust detection and identification of hardware trojans using monte-carlo dropout.

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Title: A dual-stage deep learning approach for robust detection and identification of hardware trojans using monte-carlo dropout.
Authors: Golabi, Arash1, arash.golabi@qu.edu.qa, Erradi, Abdelkarim1, erradi@qu.edu.qa, Bensaid, Ahmed1, abensaid@qu.edu.qa, Al-Ali, Abdulla1, abdulla.alali@qu.edu.qa, Qidwai, Uvais1, uqidwai@qu.edu.qa
Source: International Journal of Information Security; Jun2025, Vol. 24 Issue 3, p1-20, 20p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 186119971
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: A dual-stage deep learning approach for robust detection and identification of hardware trojans using monte-carlo dropout.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Information+Security%22">International Journal of Information Security</searchLink>; Jun2025, Vol. 24 Issue 3, p1-20, 20p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=186119971
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        Value: 10.1007/s10207-025-01049-5
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      – Code: eng
        Text: English
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              M: 06
              Text: Jun2025
              Type: published
              Y: 2025
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