Classification of Malware Network Activity
In the previous work, we have designed and implemented a platform with tools for capturing malware, running botnets in a controlled environment, analyzing their interactions with a botmaster, testing methods and techniques for mitigating botnet nuisance, and eventually disrupting them. We have used the platform to gather a large number of malware and observe its network activity.
In this paper, we present an approach to malware classification based on the observation of the malware communication behavior. First, we show that traditional methods based on antivirus tools are not suitable for classification. Then, we define the method based on observing the communication pattern of executing malware. We report on the classification results obtained with the proposed method. Unlike classification done by existing antivirus tools, the proposed method results in selective and consistent classification.
Unable to display preview. Download preview PDF.
- 3.Berger-Sabbatel, G., Korczyński, M., Duda, A.: Architecture of a Platform for Malware Analysis and Confinement. In: Proc. MCSS 2010: Multimedia Communications, Services and Security, Cracow (2010)Google Scholar
- 4.Caglayan, A., Toothaker, M., Drapaeau, D., Burke, D., Eaton, G.: Behavioral analysis of fast flux service networks. In: Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies, CSIIRW 2009, pp. 48:1–48:4. ACM, New York (2009)Google Scholar
- 5.Carsten, W., Holz, T., Freiling, F.: Toward automated dynamic malware analysis using cwsandbox. IEEE Security and Privacy 5, 32–39 (2007)Google Scholar
- 7.Leder, F., Werner, T., Martini, P.: Proactive botnet countermeasures - an offensive approach. Technical report, Institute of Computer Science IV, University of Bonn, Germany (2009)Google Scholar
- 8.Nazario, J., Holz, T.: As the net churns: Fast-flux botnet observations. In: 3rd International Conference on Malicious and Unwanted Software, Fairfax, pp. 24–31 (October 2008)Google Scholar