An Innovative and Efficient Approach for Detecting Unknown Attacks Using Feature Extraction Scheme and Fuzzy-Neural Networks

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-23 Number-2
Year of Publication : 2015
Authors : Vikas Belwal, Sandip Mandal


Vikas Belwal, Sandip Mandal "An Innovative and Efficient Approach for Detecting Unknown Attacks Using Feature Extraction Scheme and Fuzzy-Neural Networks". International Journal of Computer Trends and Technology (IJCTT) V23(2):61-64, May 2015. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
In the past two decades, internet have experienced tremendous growth that has sped up a shift in computing environments from centralized computer systems to network information systems. A large volume of valuable information such as personal profiles, banking details and other valuable information is distributed and transferred through internet. Hence, network security has become a major concern than ever. Intrusion Detection Systems (IDSs) play a crucial role in detecting various kinds of attacks and defend our computer and data from them. Therefore, intrusion detection systems are effectively used for detecting intrusion accesses. Intrusion Detection Systems have been evolved over decades and various types of systems are currently available to identify and eradicate attacks based on different system conditions and different aptitudes. Many researchers have felt the importance of new techniques other than the ones which are currently uses. Towards this direction, data mining is considered to be very handy in achieving the desired results. Among many techniques fuzzy-neural networks (FNN) are very promising in this field. In this paper we are proposing a novel approach that uses Feature Extraction Scheme, Fuzzy-Neural Networks, K-means clustering and Support Vector Machines (SVM) for better results using Kyoto2006+ dataset.

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Intrusion Detection System, Feature Extraction, Fuzzy-Neural Network, Support Vector Machines, K-means clustering.