A Statistical Approach to Malware Class Recognition

IJCA Proceedings on National Conference on Computer Science and Information Technology
© 2018 by IJCA Journal
NCCSIT 2017 - Number 1
Year of Publication: 2018
Aziz Makandar
Anita Patrot

Aziz Makandar and Anita Patrot. Article: A Statistical Approach to Malware Class Recognition. IJCA Proceedings on National Conference on Computer Science and Information Technology NCCSIT 2017(1):16-19, September 2018. Full text available. BibTeX

	author = {Aziz Makandar and Anita Patrot},
	title = {Article: A Statistical Approach to Malware Class Recognition},
	journal = {IJCA Proceedings on National Conference on Computer Science and Information Technology},
	year = {2018},
	volume = {NCCSIT 2017},
	number = {1},
	pages = {16-19},
	month = {September},
	note = {Full text available}


In this paper, we describe the proposed work on texture pattern classification using different Wavelet family, i. e. wavelet statistical features such as first order statistical feature vector. The WSF vector is formed to discriminate the various texture patterns of the Malware classes. The standard databases are used for experimental analysis of malware as a grayscale image. The database consists of 24 malware which belong to different variants with types of malware classes. The feature vector is further analyzed with malware classes the image to be classified based on the similarities in the image patterns. The experimental results shown that the efficiency of the wavelet based statistical features gives better classification results.


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