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ERIC Number: ED567940
Record Type: Non-Journal
Publication Date: 2016
Pages: 147
Abstractor: As Provided
Reference Count: N/A
ISBN: 978-1-3395-1754-4
ISSN: N/A
Relationship between Effective Application of Machine Learning and Malware Detection: A Quantitative Study
Enfinger, Kerry Wayne
ProQuest LLC, Ph.D. Dissertation, Northcentral University
The number of malicious files present in the public domain continues to rise at a substantial rate. Current anti-malware software utilizes a signature-based method to detect the presence of malicious software. Generating these pattern signatures is time consuming due to malicious code complexity and the need for expert analysis, however, by making small code changes, malicious software designers can evade detection of signature-based detection methods and render the signature useless in detecting new variations. While research into the use of computer file images and machine learning to detect malicious software shows successful results, there is a need to research alternative feature extraction and pattern detection methods to protect against adversarial techniques. The purpose of this quantitative research study, through experimental research design, was to analyze the effectiveness of the use of machine learning classification for detecting malware occurrence in computer file images as an alternative to current signature-based methods. Utilizing large samples of malware binary files and multiple kernel algorithms, this research addressed issues of maintaining both performance and high accuracy rates in the utilization of machine learning for detecting malware occurrence. This research analysis utilized a large dataset of 10,853 malware samples obtained from a well-known and respected malware repository. The use of large malware datasets improved internal validity of the research tests results by increasing both known and unknown samples while improved performance of the machine learning methodology demonstrated external validity to real-world application. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]
ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml
Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A