Abstract
Recently, the behaviour-based technique was received attentions for its ability to detect unknown viruses. However, the literature suggests that this technique still needs to be improved due to high false-positive rates. Addressing the issue, the current work-in-progress proposed an architecture utilizing the crowd for building an anti-virus knowledge base, which considers not only virus behaviour but also behaviour from the new applications. This architecture also utilized anti-virus experts in the crowd for classified objects that are unclassified by machines. Using the classified objects, it used a machine learning algorithm to analyse application behaviour from the crowd for updating the knowledge base, and thus the corresponding anti-virus system can correctly diagnose and classify objects, reducing the false-positive rates.
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References
Sukwong, O., Kim, H.S., Hoe, J.C.: Commercial antivirus software effectiveness: an empirical study. Computer 44(3), 0063–0070 (2011)
Rad, B.B., Masrom, M., Ibrahim, S.: Evolution of computer virus concealment and anti-virus techniques: a short survey. arXiv preprint arXiv:1104.1070 (2011)
Park, Y., Reeves, D.S., Stamp, M.: Deriving common malware behavior through graph clustering. Computers & Security 39, 419–430 (2013)
Bayer, U., et al.: Scalable, Behavior-Based Malware Clustering. In: NDSS. Citeseer (2009)
Egele, M., et al.: A survey on automated dynamic malware-analysis techniques and tools. ACM Computing Surveys (CSUR) 44(2), 6 (2012)
Lin, D., Stamp, M.: Hunting for undetectable metamorphic viruses. Journal in Computer Virology 7(3), 201–214 (2011)
Hu, X., Chiueh, T.-C., Shin, K.G.: Large-scale malware indexing using function-call graphs. In: Proceedings of the 16th ACM Conference on Computer and Communications Security, pp. 611–620. ACM, Chicago (2009)
Truong, M.N.Q., Hoang, T.N.: A multi-agent mechanism in machine learning approach to anti-virus system. In: Nguyen, N.T., Jo, G.-S., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2008. LNCS (LNAI), vol. 4953, pp. 743–752. Springer, Heidelberg (2008)
Rieck, K., Holz, T., Willems, C., Düssel, P., Laskov, P.: Learning and Classification of Malware Behavior. In: Zamboni, D. (ed.) DIMVA 2008. LNCS, vol. 5137, pp. 108–125. Springer, Heidelberg (2008)
Microsoft. Evolution of Malware (2014), http://www.microsoft.com/security/sir/story/default.aspx#!10year_malware
Yin, H., et al.: Panorama: capturing system-wide information flow for malware detection and analysis. In: Proceedings of the 14th ACM Conference on Computer and Communications Security. ACM (2007)
Stinson, E., Mitchell, J.C.: Characterizing Bots’ Remote Control Behavior. In: Hämmerli, B.M., Sommer, R. (eds.) DIMVA 2007. LNCS, vol. 4579, pp. 89–108. Springer, Heidelberg (2007)
Schultz, M.G., et al.: Data mining methods for detection of new malicious executables. In: Proceedings of 2001 IEEE Symposium on Security and Privacy, S&P 2001 (2001)
D32. D32 Anti-virus (2014), http://www.d32av.vn/
Howe, J.: The rise of crowdsourcing. Wired Magazine, 1–4 (2006)
Muntés-Mulero, V., Paladini, P., Manzoor, J., Gritti, A., Larriba-Pey, J.-L., Mijnhardt, F.: Crowdsourcing for industrial problems. In: Nin, J., Villatoro, D. (eds.) CitiSens 2012. LNCS, vol. 7685, pp. 6–18. Springer, Heidelberg (2013)
Chi, E.H., Bernstein, M.S.: Leveraging Online Populations for Crowdsourcing. IEEE Internet Computing 16(5), 10–12 (2012)
Zhao, Y., Zhu, Q.: Evaluation on crowdsourcing research: Current status and future direction. Information Systems Frontiers, 1–18 (2012)
Vukovic, M., Laredo, J., Rajagopal, S.: Challenges and experiences in deploying enterprise crowdsourcing service. In: Benatallah, B., Casati, F., Kappel, G., Rossi, G. (eds.) ICWE 2010. LNCS, vol. 6189, pp. 460–467. Springer, Heidelberg (2010)
Fraternali, P., et al.: Putting humans in the loop: Social computing for Water Resources Management. Environmental Modelling & Software 37, 68–77 (2012)
Corney, J., et al.: Putting the crowd to work in a knowledge-based factory. Advanced Engineering Informatics 24(3), 243–250 (2010)
Doan, A., Ramakrishnan, R., Halevy, A.Y.: Crowdsourcing systems on the world-wide web. Communications of the ACM 54(4), 86–96 (2011)
Cohen, F.: Computer viruses: theory and experiments. Computers & Security 6(1), 22–35 (1987)
Clause, J., Li, W., Orso, A.: Dytan: a generic dynamic taint analysis framework. In: Proceedings of the, International Symposium on Software Testing and Analysis. ACM (2007)
Willems, C., Holz, T., Freiling, F.: Toward automated dynamic malware analysis using cwsandbox. IEEE Security and Privacy 5(2), 32–39 (2007)
Hu, Y., et al.: Unknown malicious executables detection based on run-time behavior. In: Fifth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008. IEEE (2008)
Lanzi, A., Sharif, M.I., Lee, W.: K-Tracer: A System for Extracting Kernel Malware Behavior. In: NDSS (2009)
Rouse, A.C.: A preliminary taxonomy of crowdsourcing. In: Proceedings of the 21st Australasian Conference on Information Systems, pp. 1–10 (2010)
Kittur, A., Chi, E.H., Suh, B.: Crowdsourcing user studies with Mechanical Turk. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM (2008)
Sarasua, C., Simperl, E., Noy, N.F.: crowdMap: Crowdsourcing ontology alignment with microtasks. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 525–541. Springer, Heidelberg (2012)
Estellés-Arolas, E., González-Ladrón-de-Guevara, F.: Towards an integrated crowdsourcing definition. Journal of Information Science 38(2), 189–200 (2012)
Brabham, D.C.: Crowdsourcing. The MIT Press, Cambridge (2013)
Thuan, N.H., Antunes, P., Johnstone, D.: Factors Influencing the Decision to Crowdsource. In: Antunes, P., Gerosa, M.A., Sylvester, A., Vassileva, J., de Vreede, G.-J. (eds.) CRIWG 2013. LNCS, vol. 8224, pp. 110–125. Springer, Heidelberg (2013)
Mason, W., Suri, S.: Conducting behavioral research on Amazon’s Mechanical Turk. Behavior Research Methods 44(1), 1–23 (2012)
Brabham, D.C.: Motivations for Participation in a Crowdsourcing Application to Improve Public Engagement in Transit Planning. Journal of Applied Communication Research 40(3), 307–328 (2012)
Kingston, A.: “Choir attempted that beautiful anthem “Oh, Radiant Morn” – made a hash of it” - Making a hash of the Adkin Diary transcriptions. In: Workshop on Crowdsourcing for the Digital Humanities and Cultural Heritage Sector, Wellington, New Zealand (2013)
Brabham, D.C.: Crowdsourcing as a Model for Problem Solving: An Introduction and Cases. Convergence: The International Journal of Research into New Media Technologies 14(1), 75–90 (2008)
Vukovic, M., Bartolini, C.: Towards a research agenda for enterprise crowdsourcing. In: Margaria, T., Steffen, B. (eds.) ISoLA 2010, Part I. LNCS, vol. 6415, pp. 425–434. Springer, Heidelberg (2010)
Aitamurto, T., Leiponen, A., Tee, R.: The Promise of Idea Crowdsourcing–Benefits, Contexts, Limitations, in White Paper for Nokia IdeasProject (June 2011)
Franklin, M.J., et al.: CrowdDB: answering queries with crowdsourcing. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 61–72. ACM, Athens (2011)
McCoy, A.B., et al.: Development and evaluation of a crowdsourcing methodology for knowledge base construction: identifying relationships between clinical problems and medications. Journal of the American Medical Informatics Association 19(5), 713–718 (2012)
Wikipedia. Statistics (2014), http://en.wikipedia.org/wiki/Special:Statistics (cited June 2014)
Burguera, I., Zurutuza, U., Nadjm-Tehrani, S.: Crowdroid: behavior-based malware detection system for android. In: Proceedings of the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices. ACM (2011)
Saeed, I.A., et al.: A Survey on Malware and Malware Detection Systems. Analysis 3(10), 13–17 (2013)
Adkins, F., et al.: Heuristic malware detection via basic block comparison. In: 2013 8th International Conference on Malicious and Unwanted Software: The Americas (MALWARE). IEEE (2013)
Symantec. Submit Virus Samples (June 2014), http://www.symantec.com/security_response/submitsamples.jsp
Microsoft. Submit a sample (June 2014), https://www.microsoft.com/security/portal/submission/submit.aspx
Thuan, N.H., Antunes, P., Johnstone, D.: Toward a Nexus Model Supporting the Establishment of Business Process Crowdsourcing. In: Dang, T.K., Wagner, R., Neuhold, E., Takizawa, M., Küng, J. (eds.) FDSE 2014. LNCS, vol. 8860, pp. 136–150. Springer, Heidelberg (2014)
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Thuan, N.H., Antunes, P., Johnstone, D., Truong, M.N.Q. (2014). An Architecture Utilizing the Crowd for Building an Anti-virus Knowledge Base. In: Dang, T.K., Wagner, R., Neuhold, E., Takizawa, M., Küng, J., Thoai, N. (eds) Future Data and Security Engineering. FDSE 2014. Lecture Notes in Computer Science, vol 8860. Springer, Cham. https://doi.org/10.1007/978-3-319-12778-1_13
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DOI: https://doi.org/10.1007/978-3-319-12778-1_13
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