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Reseach Article

An Analysis of Malware Classification Technique by using Machine Learning

by P. S. S. Siva Krishna, P. Venkateswara Rao
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 50
Year of Publication: 2019
Authors: P. S. S. Siva Krishna, P. Venkateswara Rao
10.5120/ijca2019918355

P. S. S. Siva Krishna, P. Venkateswara Rao . An Analysis of Malware Classification Technique by using Machine Learning. International Journal of Computer Applications. 181, 50 ( Apr 2019), 1-4. DOI=10.5120/ijca2019918355

@article{ 10.5120/ijca2019918355,
author = { P. S. S. Siva Krishna, P. Venkateswara Rao },
title = { An Analysis of Malware Classification Technique by using Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2019 },
volume = { 181 },
number = { 50 },
month = { Apr },
year = { 2019 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number50/30495-2019918355/ },
doi = { 10.5120/ijca2019918355 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:09:40.772114+05:30
%A P. S. S. Siva Krishna
%A P. Venkateswara Rao
%T An Analysis of Malware Classification Technique by using Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 50
%P 1-4
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Development of the internet causes a major problem to the privacy and security of an organization and to personal systems. Security communities receive the huge number of malware every day, Categorization of malware to their corresponding families based on their behaviour is a complex task is to the computer security community. Traditional anti-virus systems based on the signature extraction procedures fail to classify the new malware. Therefore we propose a machine learning model to classify the malware to their corresponding families using the properties of the malware. In this paper, we present a Review of Mansour Ahmadi et al.’s Feature fusion for effective Malware Family Classification system, Liu et al.’s Automatic Malware classification and detection system, Bashari et al.’s Malware classification and detection system using ANN. Ashu Sharma et al.’s Classification of advanced Malware system. Finally, we have done a comparative analysis of all the above-mentioned methods.

References
  1. Mansour Ahmadi, Dmitry Ulyanov, Stanislav Semenov, Mikhail Trofimov, Giorgio Giacinto: Novel Feature Extraction, Selection, and Fusion for Effective Malware Family Classification.CODASPY 2016: 183-194
  2. Liu. L, Wang, B, Yu, B. et al. Frontiers Inf Technol Electronic Eng (2017) 18: 1336.
  3. Bashari Rad, Babak & Shahpasand, Maryam & Kazem Hassan Nejad, Mohammad. (2018). Malware classification and detection using the artificial neural network. Journal of Engineering Science and Technology. 14-23.
  4. Sahay, Sanjay. (2016). An effective approach for classification of advanced malware with high accuracy. International Journal of Security and its Applications. 10. 249-266.
  5. Mansour Ahmadi, Ashkan Sami, Hossein Rahimi, Babak Yadegari, Malware detection by behavioral sequential patterns, Computer Fraud & Security, Volume 2013, Issue 8,2013, Pages 11-19, ISSN 1361-3723.
  6. AnimeshPatcha, Jung-Min Park, An overview of anomaly detection techniques: Existing solutions and latest technological trends, Computer Networks, Volume 51, Issue 12, 2007, Pages 3448-3470, ISSN 1389-1286.
  7. Nir Nissim, Robert Moskovitch, LiorRokach, Yuval Elovici, Novel active learning methods for enhanced PC malware detection in windows OS, Expert Systems with Applications, Volume 41, Issue 13,2014, Pages 5843-5857, ISSN 0957-4174.
  8. Yujie Fan, Yanfang Ye, Lifei Chen, Malicious sequential pattern mining for automatic malware detection, Expert Systems with Applications, Volume 52,2016, Pages 16-25, ISSN 0957-4174.
  9. D. Gavriluţ, M. Cimpoeşu, D. Anton and L. Ciortuz, "Malware detection using machine learning," 2009 International Multiconference on Computer Science and Information Technology, Mragowo, 2009, pp. 735-741.
  10. R. Tian, R. Islam, L. Batten, and S. Versteeg, "Differentiating malware from clean ware using behavioral analysis," 2010 5th International Conference on Malicious and Unwanted Software, Nancy, Lorraine, 2010, pp. 23-30.
  11. Ronen, Royi & Radu, Marian & Feuerstein, Corina & Yom-Tov, Elad & Ahmadi, Mansour. (2018). Microsoft Malware Classification Challenge. 10.13140/RG.2.2.34695.91045.Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
  12. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
  13. Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
  14. Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
  15. Spector, A. Z. 1989. Achieving application requirements. 8. Yujie Fan, Yanfang Ye, Lifei Chen, Malicious sequential pattern mining for automatic malware detection, Expert Systems with Applications, Volume 52,2016, Pages 16-25, ISSN 0957-4174.
Index Terms

Computer Science
Information Sciences

Keywords

Windows Malware Computer Security Machine Learning Static Analysis Malware Classification Microsoft Malware Data.