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Novel Feature Selection for Non-destructive Detection of Hardware Trojans Using Hyperspectral Scanning


This paper introduces a novel method of non-destructively detecting incredibly elusive dormant hardware Trojans by selectively capturing hyperspectral backscattering measurements across the physical area of an integrated circuit. We propose a novel approach that pre-filters and actively samples an automatically selected set of the hyperspectral measurement space to significantly reduce measurement time while improving the ability to robustly detect dormant hardware Trojans. We demonstrate that our selective hyperspectral scanning approach can detect dormant hardware Trojans taking up as little as 0.03% of the circuit, which is up to 14 times smaller than prior work.

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This work has been supported, in part, by Office of Naval Research grant N00014-19-1-2287. The views and finding in this paper are those of the authors and do not reflect the views of ONR.

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Correspondence to Erik J. Jorgensen.

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Jorgensen, E.J., Kacmarcik, A., Prvulovic, M. et al. Novel Feature Selection for Non-destructive Detection of Hardware Trojans Using Hyperspectral Scanning. J Hardw Syst Secur 6, 32–46 (2022).

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  • Hardware Trojan
  • Hardware security
  • Backscattering EM side-channel
  • Active learning
  • Hyperspectral imaging