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Malware analysis method using visualization of binary files

Published:01 October 2013Publication History

ABSTRACT

Malware authors have been generating and disseminating malware variants through various ways, such as reusing modules or using automated malware generation tools. With the help of the malware generation techniques, the number of malware keeps increasing every year. Therefore, new malware analysis techniques are needed to reduce malware analysis overheads. Recently several malware visualization methods were proposed to help malware analysts. In this paper, we proposed a novel method to visually analyze malware by transforming malware binary information into image matrices. Our experimental results show that the image matrices of malware can effectively classify malware families.

References

  1. Christodorescu, M. and Jha, S., 2004. Testing malware detectors. ACM SIGSOFT Software Engineering Notes 29, 4, 34--44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Kang, B., Kim, T., Kwon, H., Choi, Y., and Im, E. G., 2012. Malware classification method via binary content comparison. In Proceedings of the 2012 ACM Research in Applied Computation Symposium ACM, 316--321. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Moser, A., Kruegel, C., and Kirda, E., 2007. Limits of static analysis for malware detection. In Proceedings of the Twenty-Third Annual IEEE Computer Security Applications Conference (ACSAC) 2007., 421--430.Google ScholarGoogle ScholarCross RefCross Ref
  4. Cesare, S. and Xiang, Y., 2010. A fast flowgraph based classification system for packed and polymorphic malware on the endhost. In Proceedings of the 24th IEEE International Conference on IEEE Advanced Information Networking and Applications (AINA), 2010, 721--728. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Kinable, J. and Kostakis, O., 2011. Malware classification based on call graph clustering. Journal in computer virology 7, 4, 233--245. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Shang, S., Zheng, N., Xu, J., Xu, M., and Zhang, H., 2010. Detecting malware variants via function-call graph similarity. In Proceedings of the 5th International Conference on IEEE Malicious and Unwanted Software (MALWARE), 2010, 113--120.Google ScholarGoogle Scholar
  7. Tabish, S. M., Shafiq, M. Z., and Farooq, M., 2009. Malware detection using statistical analysis of byte-level file content. In Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics, ACM, 23--31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Bilar, D., 2007. Opcodes as predictor for malware. International Journal of Electronic Security and Digital Forensics 1, 2, 156--168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Han, K. S., Kim, S.-R., and Im, E. G., 2012. Instruction frequency-based malware classification method. INFORMATION - An International Interdisciplinary Journal 15, 7, 2973--2984.Google ScholarGoogle Scholar
  10. Santos, I., Brezo, F., Nieves, J., Penya, Y. K., Sanz, B., Laorden, C., and Bringas, P. G., 2010. Idea: Opcode-sequence-based malware detection. Engineering Secure Software and Systems, Springer, 35--43. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Sung, A. H., Xu, J., Chavez, P., and Mukkamala, S., 2004. Static analyzer of vicious executables (save). In Proceedings of the 20th Annual IEEE Computer Security Applications Conference, 2004., 326--334. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Walenstein, A., Venable, M., Hayes, M., Thompson, C., and Lakhotia, A., 2007. Exploiting similarity between variants to defeat malware. In Proceedings of the BlackHat DC Conference.Google ScholarGoogle Scholar
  13. Trinius, P., Holz, T., Gobel, J., and Freiling, F. C., 2009. Visual analysis of malware behavior using treemaps and thread graphs. In Proceedings of the 6th International Workshop on IEEE Visualization for Cyber Security (VizSec) 2009., 33--38.Google ScholarGoogle Scholar
  14. Saxe, J., Mentis, D., and Greamo, C., 2012. Visualization of shared system call sequence relationships in large malware corpora. In Proceedings of the Ninth International Symposium on Visualization for Cyber Security, ACM, 33--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Conti, G., Dean, E., Sinda, M., and Sangster, B., 2008. Visual reverse engineering of binary and data files. Visualization for Computer Security, Springer, 1--17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Anderson, B., Storlie, C., and Lane, T., 2012. Improving malware classification: bridging the static/dynamic gap. In Proceedings of the 5th ACM workshop on Security and artificial intelligence, ACM, 3--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Nataraj, L., Karthikeyan, S., Jacob, G., and Manjunath, B., 2011. Malware images: visualization and automatic classification. In Proceedings of the 8th International Symposium on Visualization for Cyber Security,,ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Oliva, A. and Torralba, A., 2001. Modeling the shape of the scene: A holistic representation of the spatial envelope. International journal of computer vision, 42, 3, 145--175. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Torralba, A., Murphy, K. P., Freeman, W. T., and Rubin, M. A., 2003. Context-based vision system for place and object recognition. In Proceedings of the Ninth IEEE International Conference on Computer Vision, 273--280. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Nataraj, L., Yegneswaran, V., Porras, P., and Zhang, J., 2011. A comparative assessment of malware classification using binary texture analysis and dynamic analysis. In Proceedings of the 4th ACM workshop on Security and artificial intelligence, ACM, 21--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Eagle, C., 2008. The IDA Pro Book: The Unofficial Guide to the World's Most Popular Disassembler. No Starch Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Yuschuk, O., 2007. Ollydbg. http://www.ollydbg.de/Google ScholarGoogle Scholar
  23. Charikar, M. S., 2002. Similarity estimation techniques from rounding algorithms. In Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, ACM, 380--388. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. D. Bernstein. Usenet posting, comp.lang.c. http://groups.google.com/group/comp.lang.c/msg/6b82e964887d73d9, Dec. 1990.Google ScholarGoogle Scholar
  25. Androutsos, D., Plataniotis, K., and Venetsanopoulos, A. N., 1999. A novel vector-based approach to color image retrieval using a vector angular-based distance measure. Computer Vision and Image Understanding,75, 1, 46--58. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        RACS '13: Proceedings of the 2013 Research in Adaptive and Convergent Systems
        October 2013
        529 pages
        ISBN:9781450323482
        DOI:10.1145/2513228

        Copyright © 2013 ACM

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        Publication History

        • Published: 1 October 2013

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        Acceptance Rates

        RACS '13 Paper Acceptance Rate73of317submissions,23%Overall Acceptance Rate393of1,581submissions,25%

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