NetworkIntrusionDetectionSystem(NIDS), widely used network infrastructure. Although many datamining has been used to increase the effectiveness of IDSs, current ID still struggle to perform well. therfore; proposed a new NIDS focused on feature_selection. The proposed CorrelationFeatureSelection_ForestPanalizedAttributes(CFS_FPA) used for dimensionality_reduction and selects the optimal_subset. based on two steps: first check each feature with a target(class) and choose only features that most effective by applying CFS filter using a statistical_method, then applied FPA to select only features will enhance ID and reduce_dimensionality. proposal tested with the NSLKDD experimental results of accuracy 0.997% and 0.004 FAR, wherein UNSWNB15_dataset accuracy and FAR are 0.995%, 0.008 consequently.
Mhawi, Doaa Nteesha and Hashem, Prof. Soukaena H.
"Proposed Hybrid CorrelationFeatureSelectionForestPanalizedAttribute Approach to advance IDSs,"
Karbala International Journal of Modern Science: Vol. 7
, Article 15.
Available at: https://doi.org/10.33640/2405-609X.3166
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