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- W2014102125 abstract "This paper compares the application of three different dimension reduction techniques to the problem of classifying functions in object code form as being cryptographic in nature or not. A simple classifier is used to compare dimensionality reduction via sorted covariance, principal component analysis, and correlation-based feature subset selection. The analysis concentrates on the classification accuracy as the number of dimensions is increased. It is demonstrated that when discarding 90% of the measured dimensions, accuracy only suffers by 1% for this problem. By discarding dimensions, computational intelligence techniques can be applied with a drastic reduction in algorithmic complexity. The primary focus is on Intel IA32 instruction set, but analysis shows consistent results on the Sun SPARC instruction set." @default.
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- W2014102125 date "2010-05-01" @default.
- W2014102125 modified "2023-09-24" @default.
- W2014102125 title "The analysis of dimensionality reduction techniques in cryptographic object code classification" @default.
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- W2014102125 doi "https://doi.org/10.1109/hsi.2010.5514572" @default.
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