Hybrid botnet detection using ensemble approach

Title

Hybrid botnet detection using ensemble approach

Creator

Samson F.; Vaidehi V.

Description

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.

Source

Journal of Theoretical and Applied Information Technology, Vol-95, No. 8, pp. 1646-1654.

Date

2017-01-01

Publisher

Asian Research Publishing Network

Subject

Botnet; C&C; Ensemble; Hybrid Botnets; P2P

Coverage

Samson F., Department of Computer Science, Christ University, Bengaluru, India; Vaidehi V., Department of Computer Science, Christ University, Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 19928645

Format

Online

Language

English

Type

Article

Identifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018375363&partnerID=40&md5=b375e2d27c9c6acd58593e640cdb40c4