Intrusion-Miner: A Hybrid Classifier For Intrusion Detection Using Data Mining

With the rapid growth and usage of internet,number of network attacks have increase dramatically within the past few years.The problem facing in nowadays is to observe these attack efficiently for security concerns Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors,optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research Community.The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein.Consequently,it is important to monitor and handle these attacks and intrusion detection system(IDS)have potentially diagnostic ability to handle these attacks to secure the network.Numerous intrusion detection approaches are presented but the main hindrance is their performance which can be improved by increasing detection rate as well as decreasing false positive rates.Optimizing the performance of IDS is very serious issue and challenging fact that gets more and attention from the research community.In this dissertation,we proposed a hybrid classification approach Intrusion-Miner with the help of two classifier algorithm for network anomaly detection.Thus,principal component analysis(PCA)and Fisher Discriminant Ratio(FDR)have been implemented for the feature selection and noise removal.This hybrid approach is compared with J48,Bayesnet,JRip,SMO,IBK and evaluate the performance using KDD99 dataset.Experimental result revealed that the precision of the proposed approach is measured as 96.1 % with low false positive and high false negative rate as compare to other state-of-the-art algorithm.The simulation result evaluation shows that perceptible progress and real-time intrusion detection can be attained as we apply the suggested models to identify diverse kinds of network attacks.