Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

2014-09-152014-09-152014-09-15urn:nbn:de:hebis:34-2014091546010http://hdl.handle.net/123456789/2014091546010engUrheberrechtlich geschützthttps://rightsstatements.org/page/InC/1.0/Intrusion DetectionMachine LearningData MiningNetwork SecurityData AnalysisReal-time systemsMassive Data FlowsNetwork PerformanceSelf Organizing MapArtificial Neural NetworksPreprocessingFeature SelectionClassificationAdaptive SystemsData AggregationData Management004500620Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning TechniquesDissertationAlgorithmsDesignExperimentationManagementMeasurementPerformanceReliabilitySecurityTheoryVerificationMathematical MethodsNeural Networks and Related TopicsClassification MethodsCluster AnalysisSimulation ModelingDynamic AnalysisOptimization TechniquesProgramming ModelsData analysisMultivariate analysisregressionDesign of experimentsFoundations of probability theorySoftwareDiscrete mathematics in relation to computer scienceArtificial intelligenceComputing methodologies and applicationsAlgorithmsRechnernetzDatensicherungComputersicherheitEindringerkennungData MiningMaschinelles Lernen