Work place: Dept. Of Information Science & Engg, Nitte Meenakshi Institute of Technology, Bangalore, India
E-mail: Prasantagogoi24@gmail.com
Website:
Research Interests: Information Security, Network Architecture, Network Security
Biography
Prasanta Gogoi received the Ph.D. degree in Computer science and Engineering from the Tezpur University. He is currently a Professor in the College of Nitte Meenakshi institute of Technology, Bangalore, India. He is a Network-Security Researcher and Practitioner with industry and academic experience. He works closely with industry on many projects. He has published papers in reputed journals. He is an Editor on multiple Editorial Boards.
By B.A. Manjunatha Prasanta Gogoi M. T. Akkalappa
DOI: https://doi.org/10.5815/ijcnis.2019.08.01, Pub. Date: 8 Aug. 2019
Building strong IDS is essential in today’s network traffic environment, feature reduction is one approach in constructing the effective IDS system by selecting the most relevant features in detecting most known and unknown attacks. In this work, proposing the hybrid feature selection method by combining Mutual Information and Linear Correlation Coefficient techniques (MI-LCC) in producing the most efficient and optimized feature subset. Support Vector Machine (SVM) classification technique being used in accurately classifying the traffic data into normal and malicious records. The proposed framework shall be evaluated with the standard benchmarked datasets including KDD-Cup-99, NSL-KDD, and UNSW-NB15 datasets. The test results, comparison analysis and reference graphs shows that the proposed feature selection model produces optimized and most important features set for classifier to achieve stated accuracy and less false positive rate compared with other similar techniques.
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