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Problem characterization and abstraction for visual analytics in behavior-based malware pattern analysis

Published:10 November 2014Publication History

ABSTRACT

Behavior-based analysis of emerging malware families involves finding suspicious patterns in large collections of execution traces. This activity cannot be automated for previously unknown malware families and thus malware analysts would benefit greatly from integrating visual analytics methods in their process. However existing approaches are limited to fairly static representations of data and there is no systematic characterization and abstraction of this problem domain. Therefore we performed a systematic literature study, conducted a focus group as well as semi-structured interviews with 10 malware analysts to elicit a problem abstraction along the lines of data, users, and tasks. The requirements emerging from this work can serve as basis for future design proposals to visual analytics-supported malware pattern analysis.

References

  1. W. Aigner, S. Miksch, H. Schumann, and C. Tominski. Visualization of Time-Oriented Data. Springer, 2011. Google ScholarGoogle ScholarCross RefCross Ref
  2. U. Bayer, C. Kruegel, and E. Kirda. TTAnalyze: A tool for analyzing malware. In 15th Ann. Conf. Europ. Inst. Computer Antivirus Research, EICAR, 2006.Google ScholarGoogle Scholar
  3. Z. Bazrafshan, H. Hashemi, S. Fard, and A. Hamzeh. A survey on heuristic malware detection techniques. In Conf. on Info. and Knowledge Technology, pages 113--120, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  4. M. Brehmer and T. Munzner. A multi-level typology of abstract visualization tasks. TVCG, 19(12):2376--2385, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Christodorescu, S. Jha, and C. Kruegel. Mining specifications of malicious behavior. In India Software Eng. Conf., pages 5--14. ACM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. C. Collins, F. Viegas, and M. Wattenberg. Parallel tag clouds to explore and analyze faceted text corpora. In Symp. on Visual Analytics Science and Technology, pages 91--98, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  7. G. Conti. Security data visualization: graphical techniques for network analysis. No Starch Press, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. H. Dornhackl, K. Kadletz, R. Luh, and P. Tavolato. Malicious behavior patterns. In IEEE Int. Symp. on Service Oriented System Eng., pages 384--389, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. M. Egele, T. Scholte, E. Kirda, and C. Kruegel. A survey on automated dynamic malware-analysis techniques and tools. ACM Comp. Surv., 44(2):6:1--6:42, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. G. Fink, C. North, A. Endert, and S. Rose. Visualizing cyber security: Usable workspaces. In Int. Workshop on Vis. for Cyber Sec., pages 45--56, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  11. E. Gelenbe, G. Gorbil, D. Tzovaras, S. Liebergeld, D. Garcia, M. Baltatu, and G. Lyberopoulos. Security for smart mobile networks: The NEMESYS approach. In IEEE Global High Tech Congr. on Electronics, pages 63--69, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  12. J. R. Goodall, A. Komlodi, and W. G. Lutters. The work of intrusion detection: Rethinking the role of security analysts. In Proc. of the 10th Americas Conf. on Info. Systems, pages 1421--1427, NY, 2004.Google ScholarGoogle Scholar
  13. D. Gotz, H. Stavropoulos, J. Sun, and F. Wang. ICDA: A platform for intelligent care delivery analytics. AMIA Annual Symp. Proceedings, 2012:264--273, 2012.Google ScholarGoogle Scholar
  14. D. Keim. Designing pixel-oriented visualization techniques: theory and applications. TVCG, 6(1):59--78, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. Keim, J. Kohlhammer, G. Ellis, and F. Mansmann, editors. Mastering the information age: solving problems with visual analytics. Eurographics, 2010.Google ScholarGoogle Scholar
  16. A. Kerren, H. C. Purchase, and M. O. Ward, editors. Multivariate Network Visualization. LNCS 8380. Springer, Cham, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  17. H. Lam, E. Bertini, P. Isenberg, C. Plaisant, and S. Carpendale. Empirical studies in information visualization: Seven scenarios. TVCG, 18(9):1520--1536, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. S. Laxman and P. S. Sastry. A survey of temporal data mining. Sadhana, 31(2):173--198, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  19. J. Lazar, J. H. Feng, and H. Hochheiser. Research Methods in Human-Computer Interaction. Wiley, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. D. Lee, I. S. Song, K. Kim, and J.-h. Jeong. A study on malicious codes pattern analysis using visualization. In Int. Conf. on Info. Science and Applications, pages 1--5, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. M. Meyer, T. Munzner, and H. Pfister. MizBee: A multiscale synteny browser. TVCG, 15(6):897--904, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. Miksch and W. Aigner. A matter of time: Applying a data-users-tasks design triangle to visual analytics of time-oriented data. Computers & Graphics, 38:286--290, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. T. Munzner. A nested model for visualization design and validation. TVCG, 15(6):921--928, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. C. G. Nevill-Manning and I. H. Witten. Identifying hierarchical structure in sequences: A linear-time algorithm. J. Artif. Int. Res., 7(1):67--82, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. D. Quist and L. Liebrock. Visualizing compiled executables for malware analysis. In Int. Workshop on Vis. for Cyber Sec., pages 27--32, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  26. M. Sedlmair, D. Baur, S. Boring, P. Isenberg, M. Jurmu, and A. Butz. Requirements for a MDE system to support collaborative in-car communication diagnostics. In Workshop on Beyond the Laboratory: Supporting Authentic Collaboration with Multiple Displays, 2008.Google ScholarGoogle Scholar
  27. M. Sedlmair, A. Frank, T. Munzner, and A. Butz. RelEx: Visualization for actively changing overlay network specifications. TVCG, 18(12):2729--2738, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. M. Sedlmair, M. Meyer, and T. Munzner. Design study methodology: Reflections from the trenches and the stacks. TVCG, 18(12):2431--2440, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. A. Shabtai, D. Klimov, Y. Shahar, and Y. Elovici. An intelligent, interactive tool for exploration and visualization of time-oriented security data. In Int. Workshop Vis. Comp. Sec., pages 15--22. ACM, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. H. Sharp, Y. Rogers, and J. Preece. Interaction Design: Beyond Human-Computer Interaction. John Wiley & Sons, 2nd edition, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. H. Shiravi, A. Shiravi, and A. Ghorbani. A survey of visualization systems for network security. TVCG, 18(8):1313--1329, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. B. Shneiderman. The eyes have it: a task by data type taxonomy for information visualizations. In IEEE Symp. on Visual Languages, pages 336--343, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. G. Stoneburner, A. Y. Goguen, and A. Feringa. SP 800-30. risk management guide for information technology systems. Technical report, NIST, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. J. J. Thomas and K. A. Cook. Illuminating the path: The research and development agenda for visual analytics. IEEE Comp. Society Press, 2005.Google ScholarGoogle Scholar
  35. M. Tory and S. Staub-French. Qualitative analysis of visualization: A building design field study. In Proc. Workshop BEyond Time and Errors: Novel evaLuation Methods for Information Visualization, pages 7:1--7:8. ACM, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. P. Trinius, T. Holz, J. Gobel, and F. Freiling. Visual analysis of malware behavior using treemaps and thread graphs. In Int. Workshop on Vis. for Cyber Sec., pages 33--38, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  37. M. Wattenberg. Arc diagrams: Visualizing structure in strings. In Proc. IEEE Symp. Information Visualization (InfoVis), pages 110--116, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. M. Wattenberg and F. Viegas. The word tree, an interactive visual concordance. TVCG, 14(6):1221--1228, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. J. Wei and G. Salvendy. The cognitive task analysis methods for job and task design: review and reappraisal. Behaviour & Information Technology, 23(4):273--299, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  40. C. Willems, T. Holz, and F. Freiling. Toward automated dynamic malware analysis using CWSandbox. IEEE Sec. Privacy, 5(2):32--39, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. K. Wongsuphasawat and D. Gotz. Outflow: Visualizing patient flow by symptoms and outcome. In IEEE VisWeek Workshop on Visual Analytics in Healthcare, Providence, Rhode Island, USA, 2011.Google ScholarGoogle Scholar
  42. Y. Xia, K. Fairbanks, and H. Owen. Visual analysis of program flow data with data propagation. In J. R. Goodall, G. Conti, and K.-L. Ma, editors, Vis. for Comp. Sec., LNCS 5210, pages 26--35. Springer, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. D. Yao, M. Shin, R. Tamassia, and W. Winsborough. Visualization of automated trust negotiation. In IEEE Workshop on Vis. for Comp. Sec., pages 65--74, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. C. L. Yee, L. L. Chuan, M. Ismail, and N. Zainal. A static and dynamic visual debugger for malware analysis. In Asia-Pacific Conf. on Communications, pages 765--769, 2012.Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Other conferences
        VizSec '14: Proceedings of the Eleventh Workshop on Visualization for Cyber Security
        November 2014
        105 pages
        ISBN:9781450328265
        DOI:10.1145/2671491

        Copyright © 2014 ACM

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        Publication History

        • Published: 10 November 2014

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        VizSec '14 Paper Acceptance Rate12of43submissions,28%Overall Acceptance Rate39of111submissions,35%

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