A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection
Isra Al-Turaiki, Νajwa Altwaijry
Abstract
using deep feature synthesis. The performance of our models is evaluated using two benchmark data sets, namely the network security laboratory-knowledge discovery in databases data set and the University of New South Wales Network Based 2015 data set. The performance is compared with similar deep learning approaches in the literature, as well as state-of-the-art classification models. Experimental results show that our models achieve good performance in terms of accuracy and recall, outperforming similar models in the literature.
Topics & Concepts
Computer scienceConvolutional neural networkArtificial intelligenceDeep learningBenchmark (surveying)Intrusion detection systemData miningNetwork securityFeature engineeringMachine learningAnomaly detectionDimensionality reductionPreprocessorData setArtificial neural networkFeature (linguistics)Network architectureComputer securityGeographyPhilosophyLinguisticsGeodesyNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAnomaly Detection Techniques and Applications