"Proposed Hybrid CorrelationFeatureSelectionForestPanalizedAttribute Ap" by Doaa Nteesha Mhawi and Prof. Soukaena H. Hashem
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Abstract

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.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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