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Malware Detection Using Data Mining Techniques

Received: 8 October 2014     Accepted: 11 October 2014     Published: 20 October 2014
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Abstract

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.

Published in International Journal of Intelligent Information Systems (Volume 3, Issue 6-1)

This article belongs to the Special Issue Research and Practices in Information Systems and Technologies in Developing Countries

DOI 10.11648/j.ijiis.s.2014030601.16
Page(s) 33-37
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Malware, Malware Detection, Escape Techniques, Data Mining

References
[1] Ravi, C & Manoharan, R. Malware Detection using Windows Api Sequence and Machine Learning. International Journal of Computer Application, Vol.43, No.17, 2012.
[2] 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.
[3] Egele, M. S, A Survey on Automated Dynamic Malware-Analysis. ACM Computing Surveys, Vol. 44, No. 2, 2012.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] I. Gurrutxaga , Evaluation of Malware clustering based on its dynamic behaviour. Seventh Australasian Data Mining conference, Australia, pp. 163–170, 2008.
[9] 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.
[10] 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.
[11] http:// www.anubis.org
[12] http://hdasm.software.informer.com
[13] www.hex-rays.com
[14] processchecker.com/file/W32dsm89.exe.html
[15] [15]https://boveda.banamex.com.mx/englishdir/ayudas/masinfoahnlab.htm
Cite This Article
  • APA Style

    Sara Najari, Iman Lotfi. (2014). Malware Detection Using Data Mining Techniques. International Journal of Intelligent Information Systems, 3(6-1), 33-37. https://doi.org/10.11648/j.ijiis.s.2014030601.16

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    ACS Style

    Sara Najari; Iman Lotfi. Malware Detection Using Data Mining Techniques. Int. J. Intell. Inf. Syst. 2014, 3(6-1), 33-37. doi: 10.11648/j.ijiis.s.2014030601.16

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    AMA Style

    Sara Najari, Iman Lotfi. Malware Detection Using Data Mining Techniques. Int J Intell Inf Syst. 2014;3(6-1):33-37. doi: 10.11648/j.ijiis.s.2014030601.16

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  • @article{10.11648/j.ijiis.s.2014030601.16,
      author = {Sara Najari and Iman Lotfi},
      title = {Malware Detection Using Data Mining Techniques},
      journal = {International Journal of Intelligent Information Systems},
      volume = {3},
      number = {6-1},
      pages = {33-37},
      doi = {10.11648/j.ijiis.s.2014030601.16},
      url = {https://doi.org/10.11648/j.ijiis.s.2014030601.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2014030601.16},
      abstract = {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.},
     year = {2014}
    }
    

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  • TY  - JOUR
    T1  - Malware Detection Using Data Mining Techniques
    AU  - Sara Najari
    AU  - Iman Lotfi
    Y1  - 2014/10/20
    PY  - 2014
    N1  - https://doi.org/10.11648/j.ijiis.s.2014030601.16
    DO  - 10.11648/j.ijiis.s.2014030601.16
    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
    SP  - 33
    EP  - 37
    PB  - Science Publishing Group
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    AB  - 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.
    VL  - 3
    IS  - 6-1
    ER  - 

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Author Information
  • Computer Department, Payam Noor University, Tehran, Iran

  • Computer Department, Payam Noor University, Tehran, Iran

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