Malware classification is a critical part in the cybersecurity.
Traditional methodologies for the malware classification
typically use static analysis and dynamic analysis to identify malware.
In this paper, a malware classification methodology based
on its binary image and extracting local binary pattern (LBP)
features are proposed. First, malware images are reorganized into
3 by 3 grids which is mainly used to extract LBP feature. Second,
the LBP is implemented on the malware images to extract features
in that it is useful in pattern or texture classification. Finally,
Tensorflow, a library for machine learning, is applied to classify
malware images with the LBP feature. Performance comparison
results among different classifiers with different image descriptors
such as GIST, a spatial envelope, and the LBP demonstrate that
our proposed approach outperforms others.
","abstract_html":"<p>Malware classification is a critical part in the cybersecurity.</p> <p>Traditional methodologies for the malware classification</p> <p>typically use static analysis and dynamic analysis to identify malware.</p> <p>In this paper, a malware classification methodology based</p> <p>on its binary image and extracting local binary pattern (LBP)</p> <p>features are proposed. First, malware images are reorganized into</p> <p>3 by 3 grids which is mainly used to extract LBP feature. Second,</p> <p>the LBP is implemented on the malware images to extract features</p> <p>in that it is useful in pattern or texture classification. Finally,</p> <p>Tensorflow, a library for machine learning, is applied to classify</p> <p>malware images with the LBP feature. Performance comparison</p> <p>results among different classifiers with different image descriptors</p> <p>such as GIST, a spatial envelope, and the LBP demonstrate that</p> <p>our proposed approach outperforms others.</p>","abstract_has_math":false,"creators":["Luo, Jhu-Sin","Lo, Dan"],"institution":null,"degree_name":"Master of Science in Computer Science (MSCS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dan Lo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05-10T07:00:00Z","date_published":"2018-05-10T07:00:00Z","updated_at":"2026-07-24T02:43:26Z","subjects":["malware","classification","machine learning","visualization","local binary pattern","Information Security"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/cs_etd/16","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dan Lo"]},{"key":"dc:creator","label":"Author","values":["Luo, Jhu-Sin","Lo, Dan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-08-22T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science (MSCS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["malware","classification","machine learning","visualization","local binary pattern","Information Security"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/cs_etd/16"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Malware classification is a critical part in the cybersecurity.
Traditional methodologies for the malware classification
typically use static analysis and dynamic analysis to identify malware.
In this paper, a malware classification methodology based
on its binary image and extracting local binary pattern (LBP)
features are proposed. First, malware images are reorganized into
3 by 3 grids which is mainly used to extract LBP feature. Second,
the LBP is implemented on the malware images to extract features
in that it is useful in pattern or texture classification. Finally,
Tensorflow, a library for machine learning, is applied to classify
malware images with the LBP feature. Performance comparison
results among different classifiers with different image descriptors
such as GIST, a spatial envelope, and the LBP demonstrate that
our proposed approach outperforms others.
"]},{"key":"dc:title","label":"Title","values":["Malware Image Classification using Machine Learning with Local Binary Pattern"]}]}],"canonical_facts":{"dc:contributor":["Dan Lo"],"dc:creator":["Luo, Jhu-Sin","Lo, Dan"],"dc:date.available":["2018-08-22T07:00:00Z"],"dc:description.abstract":["Malware classification is a critical part in the cybersecurity.
Traditional methodologies for the malware classification
typically use static analysis and dynamic analysis to identify malware.
In this paper, a malware classification methodology based
on its binary image and extracting local binary pattern (LBP)
features are proposed. First, malware images are reorganized into
3 by 3 grids which is mainly used to extract LBP feature. Second,
the LBP is implemented on the malware images to extract features
in that it is useful in pattern or texture classification. Finally,
Tensorflow, a library for machine learning, is applied to classify
malware images with the LBP feature. Performance comparison
results among different classifiers with different image descriptors
such as GIST, a spatial envelope, and the LBP demonstrate that
our proposed approach outperforms others.
"],"dc:identifier":["https://digitalcommons.kennesaw.edu/cs_etd/16"],"dc:subject":["malware","classification","machine learning","visualization","local binary pattern","Information Security"],"dc:title":["Malware Image Classification using Machine Learning with Local Binary Pattern"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Computer Science (MSCS)"]},"updated_at":"2026-07-24T02:43:26Z"}