Malware classification using gray-scale images and ensemble learning

Malware authors can easily generate obfuscated and metamorphic malware to evade the detection of anti-virus software using automated toolkits. To identify these variations, we design an efficient system to detect and classify the malicious software. We construct a controlled disassembly files from m...

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Bibliographic Details
Published in2016 3rd International Conference on Systems and Informatics (ICSAI) pp. 1018 - 1022
Main Authors Liu Liu, Baosheng Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Bagging
Deep learning
Feature extraction
Gray-scale
Image coding
machine learning
Malware
malware classification
Malware detection
n-gram
Training
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Summary:Malware authors can easily generate obfuscated and metamorphic malware to evade the detection of anti-virus software using automated toolkits. To identify these variations, we design an efficient system to detect and classify the malicious software. We construct a controlled disassembly files from malware. And we convert disassembly files into gray-scale images. In order to improve the efficiency, we use the local mean method to compress gray-scale images, which are mapped into feature vectors. To classify malware, we propose a novel ensemble learning which is based on K-means and the diversity selection. Finally, our experiments show that our method is able to effectively classify the malware.
DOI:10.1109/ICSAI.2016.7811100