Abstract
Network intrusion detection is the process of identifying malicious activity in a network by analyzing the network traffic behavior. Data mining techniques are widely used in Intrusion Detection System (IDS) to detect anomalies. Dimensionality reduction plays a vital role in IDS, since detecting anomalies from high dimensional network traffic feature is time-consuming process. Feature selection influences the speed of the analysis and the proposed work, deploys filter and wrapper based method with firefly algorithm in the wrapper for selecting the features. The resulting features are subjected to C4.5 and Bayesian Networks (BN) based classifier with KDD CUP 99 dataset. The experimental results show that 10 features are sufficient to detect the intrusion showing improved accuracy. The proposed work is compared with the existing work showing promising improvements.
Published Version
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