Using Executable Slicing to Improve Rogue Software Detection Algorithms

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Author(s): Jan Durand (Louisiana Tech University, USA), Juan Flores (Louisiana Tech University, USA), Travis Atkison (Louisiana Tech University, USA), Nicholas Kraft (University of Alabama, USA)and Randy Smith (University of Alabama, USA)
Copyright: 2013
Pages: 12
Source title: Developing and Evaluating Security-Aware Software Systems
Source Author(s)/Editor(s): Khaled M. Khan (Qatar University, Qatar)
DOI: 10.4018/978-1-4666-2482-5.ch007

Keywords: Computer Science & IT / Information Science Reference / IT Security and Ethics / Systems & Software Design

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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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Using Executable Slicing to Improve Rogue Software Detection Algorithms

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