Hybrid Machine Learning for Network Anomaly Intrusion Detection

Zina Chkirbene, Sohaila Eltanbouly, May Bashendy, Noora Al‐Naimi, Aiman Erbad

20202020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)56 citationsDOI

Abstract

In this paper, a hybrid approach of combing two machine learning algorithms is proposed to detect the different possible attacks by performing effective feature selection and classification. This system uses Random Forest algorithm for the feature selection to find the most important features combined with Classification and Regression Trees (CART) for the classification of the different attack classes. The proposed system was tested using the UNSW-NB15 dataset and the results show that the proposed method achieves a good performance compared with the existing algorithms.

Topics & Concepts

Computer scienceFeature selectionArtificial intelligenceIntrusion detection systemRandom forestMachine learningCombingFeature (linguistics)Selection (genetic algorithm)Statistical classificationData miningDecision treeAnomaly detectionPattern recognition (psychology)LinguisticsPhilosophyGeographyCartographyNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques