Malware Detection Using Data Mining Techniques
International Journal of Intelligent Information Systems
Volume 3, Issue 6-1, December 2014, Pages: 33-37
Received: Oct. 8, 2014; Accepted: Oct. 11, 2014; Published: Oct. 20, 2014
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Sara Najari, Computer Department, Payam Noor University, Tehran, Iran
Iman Lotfi, Computer Department, Payam Noor University, Tehran, Iran
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Nowadays, malicious software attacks and threats against data and information security has become a complex process. The variety and number of these attacks and threats has resulted in providing various type of defending ways against them, but unfortunately current detection technologies are ineffective to cope with new techniques of malware designers which use them to escape from anti-malwares. In current research, we present a combination of static and dynamic methods to accelerate and improve malware detection process and to enable malware detection systems to detect malware with high precision, in less time and help network security experts to react well since time detection of security threats has a high importance in dealing with attacks.
Malware, Malware Detection, Escape Techniques, Data Mining
To cite this article
Sara Najari, Iman Lotfi, Malware Detection Using Data Mining Techniques, International Journal of Intelligent Information Systems. Special Issue: Research and Practices in Information Systems and Technologies in Developing Countries. Vol. 3, No. 6-1, 2014, pp. 33-37. doi: 10.11648/j.ijiis.s.2014030601.16
Ravi, C & Manoharan, R. Malware Detection using Windows Api Sequence and Machine Learning. International Journal of Computer Application, Vol.43, No.17, 2012.
Ravi, C & Chetia, G. Malware Threats And Mitigation Strategies: A Survey, Journal of Theoretical and Applied Information Technology, Vol. 29, No. 2, pp. 69-73, 2011.
Egele, M. S, A Survey on Automated Dynamic Malware-Analysis. ACM Computing Surveys, Vol. 44, No. 2, 2012.
Herath, H. M. P. S., & Wijayanayake, W. M. J. I. Computer Misuse in the Workplace. Journal of Business Continuity & Emergency Planning, Vol.3, No.3, P.P 259–270, 2009.
Mathur, K., and Saroj H. A Survey on Techniques in Detection and Analyzing Malware Executables. International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 44, No. 2, 2012.
Doherty, N. F., Anastasakis, L., & Fulford, H, The Information Security Policy Unpacked: A Critical Study of the Content of University Policies. International Journal of Information Management, Vol.29, No.6, pp. 449–457, 2009.
G. Tahan, L.R.Y. Automatic Malware Detection Using Common Segment Analysis and Meta-Features. Journal of Machine Learning Research, 13l, pp. 949-979, 2012.
I. Gurrutxaga , Evaluation of Malware clustering based on its dynamic behaviour. Seventh Australasian Data Mining conference, Australia, pp. 163–170, 2008.
Rieck. K, Willems.T, D¨ussel. P and Laskov. p, Learning and classification of malware behavior, 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment. Berlin, Heidelberg: Springer-Verlag, pp. 108–125, 2008.
Patel, S. C., Graham, J. H., & Ralston, P. A, Qualitatively Assessing the Vulnerability of Critical Information Systems: A New Method for Evaluating Security Eenhancements. International Journal of Information Management, Vol.28, pp. 483–491, 2008.
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