Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Vaughn, Jr. B. Rayford

Committee Member

Dampier, A. David

Committee Member

Arnett, Kirk

Committee Member

Ramkumar, Mahalingam

Committee Member

Dandass, Yoginder

Date of Degree

8-8-2009

Document Type

Dissertation - Open Access

Major

Computer Science

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

In general, the consumer must depend on others to provide their software solutions. However, this outsourcing of software development has caused it to become more and more abstract as to where the software is actually being developed and by whom, and it poses a potentially large security problem for the consumer as it opens up the possibility for rogue functionality to be injected into an application without the consumer’s knowledge or consent. This begs the question of ‘How do we know that the software we use can be trusted?’ or ‘How can we have assurance that the software we use is doing only the tasks that we ask it to do?’ Traditional methods for thwarting such activities, such as virus detection engines, are far too antiquated for today’s adversary. More sophisticated research needs to be conducted in this area to combat these more technically advanced enemies. To combat the ever increasing problem of rogue applications, this dissertation has successfully applied and extended the information retrieval techniques of n-gram analysis and document similarity and the data mining techniques of dimensionality reduction and attribute extraction. This combination of techniques has generated a more effective Trojan horse, rogue application detection capability tool suite that can detect not only standalone rogue applications but also those that are embedded within other applications. This research provides several major contributions to the field including a unique combination of techniques that have provided a new tool for the administrator’s multi-pronged defense to combat the infestation of rogue applications. Another contribution involves a unique method of slicing the potential rogue applications that has proven to provide a more robust rogue application classifier. Through experimental research this effort has shown that a viable and worthy rogue application detection tool suite can be developed. Experimental results have shown that in some cases as much as a 28% increase in overall accuracy can be achieved when comparing the accepted feature selection practice of mutual information with the feature extraction method presented in this effort called randomized projection.

URI

https://hdl.handle.net/11668/15214

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