OGEDIDS: OPPOSITIONAL GENETIC PROGRAMMING ENSEMBLE FOR DISTRIBUTED INTRUSION DETECTION SYSTEMS
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 30, Vol 5, Issue 7
Abstract
Due to the wide range application of internet and computer networks, the securing of information is indispensable one. In order to secure the information system more effectively, various distributed intrusion detection has been developed in the literature. In this paper, we utilize the oppositional genetic algorithm for Distributed Network Intrusion Detection utilizing the oppositional set based population selection mechanism. This system is mostly useful for detecting unauthorized & malicious attack in distributed network. Here, Oppositional genetic algorithm (OGA) is utilized in OGA ensemble for learning the intrusion detection behavior of networks. Also, OGA ensemble is adapted for distributed intrusion detection system by creating the network profile which classifies normal and abnormal behavior of network. For experimentation, network profile contains different classifier which uses training data set of KDD Cup 99 to generate intrusion rules. For validation, we utilize the confusion matrix, sensitivity, specificity and accuracy and the results are proved that the proposed OGEdIDS are better for intrusion detection.
Authors and Affiliations
Shrikant Vanve
Keywords
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- EP ID EP101631
- DOI 10.5281/zenodo.57737
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