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Nature Inspired Feature Selection Approach for Effective Intrusion Detection
Objectives: To reduce the dimensionality of network traffic dataset by selecting the relevant and irredundant features for accurate and quick intrusion detection. To achieve the target, we proposed a new feature selection approach based on the nature. Methods/Statistical Analysis: The proposed Modified CuttleFish Algorithm (MCFA) approach plays a crucial role in intrusion detection by selecting appropriate subset of most relevant features from huge amount of dataset. Griewank fitness function is used to calculate the fitness of the modified cuttlefish algorithm. Naive bayes classifier is employed at the generated subset of features from benchmark KDD 99 dataset in WEKA data mining tool Compare the results of proposed approach with the existing approaches of WEKA and Improved Cuttlefish Algorithm (ICFA), with different performance metrics. Findings: As per the outcomes are obtained by the WEKA Experimenter with the 9 feature selection approaches on KDD 99 10% training dataset, it has been observed that the Consistency Subset feature selection approach with Greedy stepwise search method gives higher accurate results than other approaches and from the literature survey has been found that the ICFA performs well, but still there is problem of low True positive (TP) rate and False negative (FN) rate. These problems are addressed by the proposed feature selection approach which outer perform best from others in accuracy rate (91.79%), True positive rate (0.947), false positive rate (0.025) and ROC area (0.9982) with the minimum amount of time at 19 relevant subset of features instead 41 features. As the consequence, the proposed approach is novel from existing approaches to increase the intrusion detection rate and discard the redundant and irrelevant subset of features. Application/Improvements: MCFA has improved intrusion detection rate by increasing the TP rate and decreasing the FP rate, so MCFA can be used for the real time applications of intrusion detection system.
Accuracy, Dimensionality, Feature Selection Approach, FP Rate, Intrusion Detection, Modified Cuttlefish Algorithm, TP Rate.
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