Abstract
The static worm propagation model can not accurately describe the propagation of worm. This paper analyzes worm non-linear propagation models, draws out the worm propagation trend and proposes a new dynamic worm non-linear propagation model. Then the worm feature detection technology is designed based on the worm non-linear propagation models. The system uses rule-based detection method to monitor network worms, and gives alarms to server. Experimental results show that the scheme is a good solution to worm detection in multiple network environments and possess with higher detection rate and lower false alarm rate.
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References
Xu, Y.-g., Qian, H.-y., Li, H.-f.: Routing worm propagation model in IPv6 networks. Application Research of Computers 3920 (2009)
Zhang, D.-x., Peng, J., Hong, H.E.: Research the Propagation Model of Internet Worm. Network Communication and Security, 1244–1246 (2007)
Zhang., X.-y., Qing, S.-h., Li, Q., Li, D.-z., He, C.-h.: A Coordinated Worm Detection Method Based on Local Nets. Journal of Software, 412–421 (2007)
Zhao, g., Zhang, t.: Design of the worm detection system Based on the worm propagation characteristics. Computer Security, 114–118 (2009)
Ram, D., Cangussu, W., Sudeep, P.: Fast Worm Containment Using Feedback Control. Dependable and Secure Computing 5(2), 119–136 (2007)
Kim, H.A., Karp, B.: Autograph. Toward Automated, Distributed Worm Signature Detection. In: Proceedings of the 13th USENIX Security Symposium, San Diego, CA, pp. 59–66 (2004)
Dagon, D., Qin, X., Gu, G., Lee, W., Grizzard, J.B., Levine, J.G., Owen, H.L.: HoneyStat: Local worm detection using honeypots. In: Jonsson, E., Valdes, A., Almgren, M. (eds.) RAID 2004. LNCS, vol. 3224, pp. 39–58. Springer, Heidelberg (2004)
Tang, Y., Chen, S.: Defending against internet worms: A signature-based approach. In: Proceedings of IEEE INFOCOM 2005, Hong Kong, pp. 13–23 (2005)
Eugene, H.: The Internet worm programs. ACM Computer 23(3), 17–57 (1989)
Wang, Y., Wang, C.X.: Modeling The Effects of Timing Parameters on Virus Propagation. In: Proceedings of the ACM CCS Workshop on Rapid Malcode (WORM 2003), pp. 61–66. ACM press, Washington (2003)
Dantu, R., Cangussu, J., Yelimeli, A.: Dynamic control of worm propagation. Information Technology 1(3), 419–423 (2004)
Streftaris, G., Gibson, G.J.: Statistical Inference for Stochastic Epidemic Models. In: Proceedings of the 17th International Workshop on Statistical Modeling, China, pp. 609–616 (2002)
Zou, C.C., Gong, W., Towsley, D.: Code Red Worm Propagation Modeling and Analysis. In: Proceedings of the 9th ACM Symp on Computer and Communication Security, pp. 138–147. ACM Press, Washington (2002)
Bishop, M.: A Model of Security Monitoring. In: Proceedings of Fifth AnnualComputer Security Applications Conference, New Orleans, pp. 249–251. IEEE Computer Society, Washington DC, USA (1989)
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Tong, X., Wang, Z. (2012). Worm Nonlinear Model Optimization and Feature Detection Technology. In: Sénac, P., Ott, M., Seneviratne, A. (eds) Wireless Communications and Applications. ICWCA 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 72. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29157-9_15
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DOI: https://doi.org/10.1007/978-3-642-29157-9_15
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