Research on Malware Classification with Network Activity for Classification and Attack Prediction of Attack Groups 


Vol. 42,  No. 1, pp. 193-204, Jan.  2017


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  Abstract

The security of Internet systems critically depends on the capability to keep anti-virus (AV) software up-to-date and maintain high detection accuracy against new malware. However, malware variants evolve so quickly they cannot be detected by conventional signature-based detection. In this paper, we proposed a malware classification method based on sequence patterns generated from the network flow of malware samples. We evaluated our method with 766 malware samples and obtained a classification accuracy of approximately 40.4%. In this study, malicious codes were classified only by network behavior of malicious codes, excluding codes and other characteristics. Therefore, this study is expected to be further developed in the future. Also, we can predict the attack groups and additional attacks can be prevented.

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  Cite this article

[IEEE Style]

H. Lim, W. Kim, H. Noh, J. Lim, "Research on Malware Classification with Network Activity for Classification and Attack Prediction of Attack Groups," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 1, pp. 193-204, 2017. DOI: .

[ACM Style]

Hyo-young Lim, Wan-ju Kim, Hong-jun Noh, and Jae-sung Lim. 2017. Research on Malware Classification with Network Activity for Classification and Attack Prediction of Attack Groups. The Journal of Korean Institute of Communications and Information Sciences, 42, 1, (2017), 193-204. DOI: .

[KICS Style]

Hyo-young Lim, Wan-ju Kim, Hong-jun Noh, Jae-sung Lim, "Research on Malware Classification with Network Activity for Classification and Attack Prediction of Attack Groups," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 1, pp. 193-204, 1. 2017.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880