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Intelligent feature selection and classification techniques for intrusion detection in networks: A survey
, K. Kulothungan, S. Muthurajkumar, M. Vijayalakshmi, L. Yogesh,
Published in
2013
Volume: 2013
   
Issue: 1
Abstract
Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper. © 2013 Stolojescu-Crisan and Isar; licensee Springer.
About the journal
JournalEurasip Journal on Wireless Communications and Networking
ISSN16871472