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Using Executable Slicing to Improve Rogue Software Detection Algorithms

Using Executable Slicing to Improve Rogue Software Detection Algorithms

Jan Durand, Juan Flores, Travis Atkison, Nicholas Kraft, Randy Smith
Copyright: © 2011 |Volume: 2 |Issue: 2 |Pages: 12
ISSN: 1947-3036|EISSN: 1947-3044|EISBN13: 9781613508961|DOI: 10.4018/jsse.2011040103
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MLA

Durand, Jan, et al. "Using Executable Slicing to Improve Rogue Software Detection Algorithms." IJSSE vol.2, no.2 2011: pp.53-64. http://doi.org/10.4018/jsse.2011040103

APA

Durand, J., Flores, J., Atkison, T., Kraft, N., & Smith, R. (2011). Using Executable Slicing to Improve Rogue Software Detection Algorithms. International Journal of Secure Software Engineering (IJSSE), 2(2), 53-64. http://doi.org/10.4018/jsse.2011040103

Chicago

Durand, Jan, et al. "Using Executable Slicing to Improve Rogue Software Detection Algorithms," International Journal of Secure Software Engineering (IJSSE) 2, no.2: 53-64. http://doi.org/10.4018/jsse.2011040103

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Abstract

This paper describes a research effort to use executable slicing as a pre-processing aid to improve the prediction performance of rogue software detection. The prediction technique used here is an information retrieval classifier known as cosine similarity that can be used to detect previously unknown, known or variances of known rogue software by applying the feature extraction technique of randomized projection. This paper provides direction in answering the question of is it possible to only use portions or subsets, known as slices, of an application to make a prediction on whether or not the software contents are rogue. This research extracts sections or slices from potentially rogue applications and uses these slices instead of the entire application to make a prediction. Results show promise when applying randomized projections to cosine similarity for the predictions, with as much as a 4% increase in prediction performance and a five-fold decrease in processing time when compared to using the entire application.

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