Publication:
Attributes and machine learning for fragment identification and malware analysis

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Authors
Beneduce, Kristen
Subjects
Machine Learning
Information Theory
File Forensics
Malware Detection
Digital Fingerprinting
Anomaly Detection
Advisors
Young, Joel
Date of Issue
2014-09
Date
Sep-14
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
This study applies machine learning techniques and novel statistical features for two important classification problems in secure computing: malware detection and file fragment type identification. We observe combinations of information-theoretic and Natural Language Processing features extracted from byte level file content. To the extent possible, we replicate recent studies to validate the use of these features and expand on recent work by combining features from malware to detection to fragment identification tasks and vice versa. By avoiding the use of extracted file signatures and strings, this study contributes techniques that may be more resistant to obfuscation attacks, lead to enhanced prediction rates for zero-day malware files, and improved forensics on broken fragments where file metadata information is not available. We evaluate our results against recent works and report the highest performing algorithms and combinations of features for each task.
Type
Thesis
Description
Series/Report No
Department
Computer Science
Organization
Identifiers
NPS Report Number
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Funder
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Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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