Behaviour analysis of machine learning algorithms for detecting P2P botnets

Author

Garg, Shelly ; Singh, A.K. ; Sarje, Anil K. ; Peddoju, Sateesh K.

Author_Institution

Dept. of Comput. Sci. & Eng., Indian Inst. of Technol. Roorkee, Roorkee, India

fYear

2013

fDate

21-22 Sept. 2013

Firstpage

1

Lastpage

4

Abstract

Botnets have emerged as a powerful threat on the Internet as it is being used to carry out cybercrimes. In this paper, we have analysed some machine learning techniques to detect peer to peer (P2P) botnets. As the detection of P2P botnets is widely unexplored area, we have focused on it. We experimented with different machine learning (ML) algorithms to compare their ability to classify the botnet traffic from the normal traffic by selecting distinguishing features of the network traffic. Experiments are performed on the dataset containing the traces of various P2P botnets. Results and tradeoffs obtained of different ML algorithms on different metrics are presented at the end of the paper.

Keywords

Internet; computer crime; computer network security; invasive software; learning (artificial intelligence); peer-to-peer computing; telecommunication computing; telecommunication traffic; Internet; P2P botnet detection; botnet traffic classification; cybercrimes; feature selection; machine learning algorithms; machine learning techniques; network traffic; peer to peer botnet detection; Algorithm design and analysis; Classification algorithms; Data mining; Feature extraction; Niobium; Testing; Training; Behavior Analysis; Command & control; Machine learning; Network Security; P2P; P2P botnet;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computing Technologies (ICACT), 2013 15th International Conference on

Conference_Location

Rajampet

Print_ISBN

978-1-4673-2816-6

Type

conf

DOI

10.1109/ICACT.2013.6710523

Filename

6710523