Hybrid botnet detection using ensemble approach
Botnet; C&C; Ensemble; Hybrid Botnets; P2P
Botnets are one of the most threatening cyber-attacks available today. This paper proposes a hybrid system which can effectively detect the presence of C&C, P2P and hybrid botnets in the network. The powerful machine learning algorithms like BayesNet, IBk, KStar, J48 and Random Tree have been deployed for detecting these malwares. The performance and accuracy of the individual classifiers are compared with the ensemble approach. Labelled dataset of botnet logs were collected from the Malware Facility. Secured data was collected from Christ university network and the combined dataset is tested using virtual test bed. The performance of the algorithms is studied in this paper. Ensemble approach out performed individual classifiers. 2005 ongoing JATIT & LLS.
Journal of Theoretical and Applied Information Technology, Vol-95, No. 8, pp. 1646-1654.
Samson F., Department of Computer Science, Christ University, Bengaluru, India; Vaidehi V., Department of Computer Science, Christ University, Bengaluru, India