Internet Worm Detection based on Traffic Behavior Monitoring with Improved C4.5

S Divya¹, G Padmavathi²

¹Research scholar, ²Professor and Head, Department of Computer Science, Avinashilingam University, Coimbatore, India

Abstract

Internet worms are identified as one of the serious security threats caused by their anomalous behaviors. Worms in the network cause many cyber security threats such as distributed denial of Service, illegal network traffic, spreading spam and stealing personal user information. In this paper, the network traffic is monitored and their abnormal behavior on the Internet is detected and classified based on attribute payload using C4.5 algorithm with Pearson's Correlation Coefficient. The proposed approach detects the Internet worm activities based on their traffic behavior using a decision tree algorithm. The categorization of continuous attributes is performed by C4.5 algorithm to test the distinct values and the method reduces the processing time of the large input. The experimental results obtained identify the unknown worms with improved accuracy in detecting malicious flows. The results obtained show improved precision value; recall value and better accuracy detection of Internet worms.

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Cite this Article as Follows

S Divya, G Padmavathi. Internet Worm Detection based on Traffic Behavior Monitoring with Improved C4.5. International Conference on Cryptography and Security 2014. Vol. 1. Chennai: Association of Scientists, Developers and Faculties, 2014. 48-56. Print.