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Mrinal Kanti Ghose

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

E-mail: mkghose2000@yahoo.com

Website:

Research Interests: Software Engineering, Image Compression, Image Manipulation, Information Security, Network Architecture, Network Security, Image Processing, Data Mining, Information-Theoretic Security

Biography

Prof. (Dr.) M.K.Ghose is currently the Dean (R & D), SMIT and Professor and Head of the Department of Computer Science & Engineering at Sikkim Manipal Institute of Technology, Majitar, Sikkim, India since June, 2006. During June 2008 to June 2010, he had also carried out additional responsibilities of Head, SMU-IT. Prior to this, Dr. Ghose worked in the internationally reputed R & D organization ISRO – during 1981 to 1994 at Vikram Sarabhai Space Centre, ISRO, Trivandrum in the areas of Mission simulation and Quality & Reliability Analysis of ISRO Launch vehicles and Satellite systems and during 1995 to 2006 at Regional Remote Sensing Service Centre, ISRO, IIT Campus, Kharagpur in the areas of RS & GIS techniques for the natural resources management. His areas of research interest are Data Mining, Simulation & Modeling, Network, Sensor Network, Information Security, Optimization & Genetic Algorithm, Digital Image processing, Remote Sensing & GIS and Software Engineering and published 221 research papers in various national and international journals. Till date, he has produced 8 Ph.Ds and research assistance given for 2 Ph.Ds. Presently 11 scholars are pursuing Ph.D work under his guidance.

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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