Malware Detection Using Data Mining Techniques

Sara Najari; Iman Lotfi

doi:doi:10.11648/j.ijiis.s.2014030601.16

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Malware Detection Using Data Mining Techniques

Sara Najari, Iman Lotfi

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

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), 2024. 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

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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
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2014030601.16
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

Sara Najari

Computer Department, Payam Noor University, Tehran, Iran
Iman Lotfi

Computer Department, Payam Noor University, Tehran, Iran

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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}
}

Copy | Download

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
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2014030601.16
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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