The Analysis of Dimensionality Reduction Techniques in Cryptographic Object Code Classification

被引：0

作者：

Wright, Jason L. ^{[1
]}

Manic, Milos ^{[2
]}

机构：

[1] Idaho Natl Lab, Idaho Falls, ID 83402 USA

[2] Univ Idaho, Idaho Falls, ID USA

来源：

3RD INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTION | 2010年

关键词：

correlation-based feature subset selection; cryptography; dimensionality reduction; principal component analysis (PCA); sorted covariance;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：157 / 162

页数：6