Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR)

Anuj Lohani; Aditi Lohani; Jitendra Singh; Manish Bhardwaj

doi:doi:10.11648/j.ajnc.20150403.14

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Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR)

Anuj Lohani, Aditi Lohani, Jitendra Singh, Manish Bhardwaj

Published in American Journal of Networks and Communications (Volume 4, Issue 3)

Received: 18 March 2015 Accepted: 6 April 2015 Published: 11 May 2015

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Abstract

Now a day’s malware are one of the major threats to computer information system. The current malware detection technologies have certain significant limitations on their part. Different organizations which deal with the protection of sensitive information may face the problem in identifying recent malware threats among millions and billions of benign executables using just signature-based antivirus systems. Currently for frontline defense against malware, signature-based antivirus products are used by organization.In the undergoing project, we proposed a detection approach by using static heuristics in MATR for malware in PE (portable executable) files. The project suggestslarger performance-based malware target recognition architecture that at present use only static heuristic features.Results of the experiments show that this architecture achieves an overall test accuracy of greater than 98% againstmalware set collected from various operational environments, while most antivirus provide detection accuracy of only 60% at their most sensitive configuration [1]. Implementations of this architecture enables benign executables to be classified successfully to some extent providing enhanced awareness of operators in hostile environments it also enable detection of unknown malware. We are to show the performance of Bagging and AdaBoostensemble.

Published in	American Journal of Networks and Communications (Volume 4, Issue 3)
DOI	10.11648/j.ajnc.20150403.14
Page(s)	44-48
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), 2015. Published by Science Publishing Group

Keywords

Malware, PE (Portable Executable), Bagging, AdaBoost (Adaptive Boosting)

References

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[7]	S. Treadwell and M. Zhou, “A heuristic approach for detection of obfuscated malware,” in Proc. Intell. Security Inform., Jun. 2009, pp. 291–299.
[8]	Jinrong Bai, Junfeng Wang, and Guozhong Zou, “A Malware Detection Scheme Based on Mining Format Information,” The Scientific World Journal Volume 2014, Article ID 260905, 11 pages.
[9]	A. Shabtai, R.Moskovitch, Y. Elovici, and C. Glezer, “Detection of malicious code by applying machine learning classifiers on static features: a state-of-the-art survey,” Information Security Technical Report, vol. 14, no. 1, pp. 16–29, 2009.
[10]	J. Z. KolterandM. A.Maloof, “Learning to detect and classify malicious executables in the wild,” Journal of Machine Learning Research, vol. 7, pp. 2721–2744, 2006.
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[12]	W. Arnold and G. Tesauro, “Automatically generated Win32 heuristic virus detection,” in Proc. Virus Bull. Conf., Sep. 2000, pp. 51–60.
[13]	G. Tesauro, J. Kephart, and G. Sorkin, “Neural networks for computer virus recognition,” IEEE Expert, vol. 11, no. 4, pp. 5–6, Aug. 1996.
[14]	T. E. Dube, R. A. Raines, S. K. Rogers, “Malware Target Recognition,” United States Patent Application Publication [US 2012/0260342 A1], 2012.
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[17]	VX Heavens. (2010, Apr. 15). Virus Collection [Online]. Available: vx.netlux.org/vl.php
[18]	T. E. Dube, “A NOVEL MALWARE TARGET RECOGNITION ARCHITECTURE FOR ENHANCED CYBERSPACE SITUATION AWARENESS,” Air Force Institute of Technology, AFIT/DCE/ENG/11-07, September 2011.

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APA Style

Anuj Lohani, Aditi Lohani, Jitendra Singh, Manish Bhardwaj. (2015). Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR). American Journal of Networks and Communications, 4(3), 44-48. https://doi.org/10.11648/j.ajnc.20150403.14

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ACS Style

Anuj Lohani; Aditi Lohani; Jitendra Singh; Manish Bhardwaj. Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR). Am. J. Netw. Commun. 2015, 4(3), 44-48. doi: 10.11648/j.ajnc.20150403.14

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AMA Style

Anuj Lohani, Aditi Lohani, Jitendra Singh, Manish Bhardwaj. Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR). Am J Netw Commun. 2015;4(3):44-48. doi: 10.11648/j.ajnc.20150403.14

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@article{10.11648/j.ajnc.20150403.14,
  author = {Anuj Lohani and Aditi Lohani and Jitendra Singh and Manish Bhardwaj},
  title = {Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR)},
  journal = {American Journal of Networks and Communications},
  volume = {4},
  number = {3},
  pages = {44-48},
  doi = {10.11648/j.ajnc.20150403.14},
  url = {https://doi.org/10.11648/j.ajnc.20150403.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20150403.14},
  abstract = {Now a day’s malware are one of the major threats to computer information system. The current malware detection technologies have certain significant limitations on their part. Different organizations which deal with the protection of sensitive information may face the problem in identifying recent malware threats among millions and billions of benign executables using just signature-based antivirus systems. Currently for frontline defense against malware, signature-based antivirus products are used by organization.In the undergoing project, we proposed a detection approach by using static heuristics in MATR for malware in PE (portable executable) files. The project suggestslarger performance-based malware target recognition architecture that at present use only static heuristic features.Results of the experiments show that this architecture achieves an overall test accuracy of greater than 98% againstmalware set collected from various operational environments, while most antivirus provide detection accuracy of only 60% at their most sensitive configuration [1]. Implementations of this architecture enables benign executables to be classified successfully to some extent providing enhanced awareness of operators in hostile environments it also enable detection of unknown malware. We are to show the performance of Bagging and AdaBoostensemble.},
 year = {2015}
}

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TY  - JOUR
T1  - Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR)
AU  - Anuj Lohani
AU  - Aditi Lohani
AU  - Jitendra Singh
AU  - Manish Bhardwaj
Y1  - 2015/05/11
PY  - 2015
N1  - https://doi.org/10.11648/j.ajnc.20150403.14
DO  - 10.11648/j.ajnc.20150403.14
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 44
EP  - 48
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20150403.14
AB  - Now a day’s malware are one of the major threats to computer information system. The current malware detection technologies have certain significant limitations on their part. Different organizations which deal with the protection of sensitive information may face the problem in identifying recent malware threats among millions and billions of benign executables using just signature-based antivirus systems. Currently for frontline defense against malware, signature-based antivirus products are used by organization.In the undergoing project, we proposed a detection approach by using static heuristics in MATR for malware in PE (portable executable) files. The project suggestslarger performance-based malware target recognition architecture that at present use only static heuristic features.Results of the experiments show that this architecture achieves an overall test accuracy of greater than 98% againstmalware set collected from various operational environments, while most antivirus provide detection accuracy of only 60% at their most sensitive configuration [1]. Implementations of this architecture enables benign executables to be classified successfully to some extent providing enhanced awareness of operators in hostile environments it also enable detection of unknown malware. We are to show the performance of Bagging and AdaBoostensemble.
VL  - 4
IS  - 3
ER  -

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Author Information

Anuj Lohani

Dept. of Computer Science and Engineering, SRM University, NCR Campus, Modinagar, India
Aditi Lohani

Dept. of Computer Science and Engineering, SRM University, NCR Campus, Modinagar, India
Jitendra Singh

Dept. of Computer Science and Engineering, SRM University, NCR Campus, Modinagar, India
Manish Bhardwaj

Dept. of Computer Science and Engineering, SRM University, NCR Campus, Modinagar, India

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APA Style

Anuj Lohani, Aditi Lohani, Jitendra Singh, Manish Bhardwaj. (2015). Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR). American Journal of Networks and Communications, 4(3), 44-48. https://doi.org/10.11648/j.ajnc.20150403.14

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ACS Style

Anuj Lohani; Aditi Lohani; Jitendra Singh; Manish Bhardwaj. Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR). Am. J. Netw. Commun. 2015, 4(3), 44-48. doi: 10.11648/j.ajnc.20150403.14

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AMA Style

Anuj Lohani, Aditi Lohani, Jitendra Singh, Manish Bhardwaj. Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR). Am J Netw Commun. 2015;4(3):44-48. doi: 10.11648/j.ajnc.20150403.14

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@article{10.11648/j.ajnc.20150403.14,
  author = {Anuj Lohani and Aditi Lohani and Jitendra Singh and Manish Bhardwaj},
  title = {Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR)},
  journal = {American Journal of Networks and Communications},
  volume = {4},
  number = {3},
  pages = {44-48},
  doi = {10.11648/j.ajnc.20150403.14},
  url = {https://doi.org/10.11648/j.ajnc.20150403.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20150403.14},
  abstract = {Now a day’s malware are one of the major threats to computer information system. The current malware detection technologies have certain significant limitations on their part. Different organizations which deal with the protection of sensitive information may face the problem in identifying recent malware threats among millions and billions of benign executables using just signature-based antivirus systems. Currently for frontline defense against malware, signature-based antivirus products are used by organization.In the undergoing project, we proposed a detection approach by using static heuristics in MATR for malware in PE (portable executable) files. The project suggestslarger performance-based malware target recognition architecture that at present use only static heuristic features.Results of the experiments show that this architecture achieves an overall test accuracy of greater than 98% againstmalware set collected from various operational environments, while most antivirus provide detection accuracy of only 60% at their most sensitive configuration [1]. Implementations of this architecture enables benign executables to be classified successfully to some extent providing enhanced awareness of operators in hostile environments it also enable detection of unknown malware. We are to show the performance of Bagging and AdaBoostensemble.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Static Heuristics Classifiers as Pre-Filter for Malware Target Recognition (MATR)
AU  - Anuj Lohani
AU  - Aditi Lohani
AU  - Jitendra Singh
AU  - Manish Bhardwaj
Y1  - 2015/05/11
PY  - 2015
N1  - https://doi.org/10.11648/j.ajnc.20150403.14
DO  - 10.11648/j.ajnc.20150403.14
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 44
EP  - 48
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20150403.14
AB  - Now a day’s malware are one of the major threats to computer information system. The current malware detection technologies have certain significant limitations on their part. Different organizations which deal with the protection of sensitive information may face the problem in identifying recent malware threats among millions and billions of benign executables using just signature-based antivirus systems. Currently for frontline defense against malware, signature-based antivirus products are used by organization.In the undergoing project, we proposed a detection approach by using static heuristics in MATR for malware in PE (portable executable) files. The project suggestslarger performance-based malware target recognition architecture that at present use only static heuristic features.Results of the experiments show that this architecture achieves an overall test accuracy of greater than 98% againstmalware set collected from various operational environments, while most antivirus provide detection accuracy of only 60% at their most sensitive configuration [1]. Implementations of this architecture enables benign executables to be classified successfully to some extent providing enhanced awareness of operators in hostile environments it also enable detection of unknown malware. We are to show the performance of Bagging and AdaBoostensemble.
VL  - 4
IS  - 3
ER  -

Copy | Download