Data Preparation and Pre-processing of Intrusion Detection Datasets using Machine Learning
Gayatri Ketepalli, Premamayudu Bulla
Abstract
This research study explores various data pre-processing approaches for NIDS (Network Intrusion Detection Systems) datasets. Pre-processing aims to prepare the data for further analysis and modeling and improve the performance and accuracy of NIDS. The paper examines different techniques for cleaning, transforming, and normalizing the data, including feature selection, feature extraction, and feature scaling. The article also discusses these approaches' challenges and limitations and their impact on NIDS performance. The research concludes by comparing different pre-processing methods and recommendations for future research.
Topics & Concepts
Computer scienceFeature selectionIntrusion detection systemFeature extractionData processingArtificial intelligenceData miningMachine learningIntrusionFeature (linguistics)DatabasePhilosophyLinguisticsGeologyGeochemistryNetwork Security and Intrusion DetectionAnomaly Detection Techniques and ApplicationsSmart Grid Security and Resilience