Pijush Barthakur

Work place: Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim, India

E-mail: pijush.barthakur@gmail.com


Research Interests: Network Security, Data Mining, Data Structures and Algorithms


Pijush Barthakur received the Master of Computer Application (M.C.A) degree from Dibrugarh University, India in 2001. Currently he is working as associate professor at Department of Computer Science & Engineering, Sikkim Manipal Institute of Technology, Sikkim, India. He is also pursuing his doctoral degree at Sikkim Manipal University. His research interests lie in the area of Network Security and Data Mining. He is currently a member of Technical Program Committee at 5th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Beijing, China, 2013.

Author Articles
An Efficient Machine Learning Based Classification Scheme for Detecting Distributed Command & Control Traffic of P2P Botnets

By Pijush Barthakur Manoj Dahal Mrinal Kanti Ghose

DOI: https://doi.org/10.5815/ijmecs.2013.10.02, Pub. Date: 8 Oct. 2013

Biggest internet security threat is the rise of Botnets having modular and flexible structures. The combined power of thousands of remotely controlled computers increases the speed and severity of attacks. In this paper, we provide a comparative analysis of machine-learning based classification of botnet command & control(C&C) traffic for proactive detection of Peer-to-Peer (P2P) botnets. We combine some of selected botnet C&C traffic flow features with that of carefully selected botnet behavioral characteristic features for better classification using machine learning algorithms. Our simulation results show that our method is very effective having very good test accuracy and very little training time. We compare the performances of Decision Tree (C4.5), Bayesian Network and Linear Support Vector Machines using performance metrics like accuracy, sensitivity, positive predictive value(PPV) and F-Measure. We also provide a comparative analysis of our predictive models using AUC (area under ROC curve). Finally, we propose a rule induction algorithm from original C4.5 algorithm of Quinlan. Our proposed algorithm produces better accuracy than the original decision tree classifier.

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