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Analysis of Machine Learning Techniques used in Malware Classification in Cloud Computing Environment

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Ajeet Kumar, Naman Sharma, Abhishek Khanna, Saurav Gandhi
10.5120/ijca2016908184

Ajeet Kumar, Naman Sharma, Abhishek Khanna and Saurav Gandhi. Article: Analysis of Machine Learning Techniques used in Malware Classification in Cloud Computing Environment. International Journal of Computer Applications 133(15):15-18, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Ajeet Kumar and Naman Sharma and Abhishek Khanna and Saurav Gandhi},
	title = {Article: Analysis of Machine Learning Techniques used in Malware Classification in Cloud Computing Environment},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {15},
	pages = {15-18},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Study the behavior of malicious software, understand the security challenges, detect the malware behavior automatically using dynamic approach. Study various classification techniques and to group these malwares and able to cluster different malware into unknown group whose characteristics are not known. The classifiers used in this research are k-Nearest Neighbors (kNN), J48 Decision Tree, and n-grams. Based on the analysis of the tests and experimental results of all the 3 classifiers, the overall best performance was achieved by J48 decision tree with a recall of 96.3%.

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Keywords

Malware, Opcode n-grams, Bytecode n-grams, malware behaviors; malware classification.