University of Bahrain
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Network Based Intrusion Detection Using the UNSW-NB15 Dataset

Show simple item record Meftah, Souhail Rachidi, Tajjeeddine Assem, Nasser 2019-09-01T19:16:34Z 2019-09-01T19:16:34Z 2019-09-01
dc.identifier.issn 2210-142X
dc.description.abstract In this work, we apply a two stage anomaly-based network intrusion detection process using the UNSW-NB15 dataset. We use Recursive Feature Elimination and Random Forests among other techniques to select the best dataset features for the purpose of machine learning; then we perform a binary classification in order to identify intrusive traffic from normal one, using a number of data mining techniques, including Logistic Regression, Gradient Boost Machine, and Support Vector Machine. Results of this first stage classification show that the use of Support Vector Machine reports the highest accuracy (82.11%). We then feed the output of Support Vector Machine to a range of multinomial classifiers in order to improve the accuracy of predicting the type of attacks. Specifically, we evaluate the performance of Decision Trees (C5.0), Naïve Bayes and multinomial Support Vector Machine. Applying C5.0 yielded the highest accuracy (74%) and F1 score (86%), and the two-stage hybrid classification improved the accuracy of results by up to 12% (achieving a multi-classification accuracy of 86.04%). Finally, with the support of our results, we present constructive criticism of the UNSW-NB15 dataset. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution 4.0 International *
dc.rights.uri *
dc.subject Intrusion Detection en_US
dc.subject NIDS en_US
dc.subject UNSW-NB15 en_US
dc.subject Data Mining en_US
dc.subject Decision Trees en_US
dc.subject SVM en_US
dc.subject Naïve Bayes en_US
dc.subject GBM en_US
dc.subject Logistic Regression en_US
dc.subject Attack Detection en_US
dc.subject Cybersecurity en_US
dc.title Network Based Intrusion Detection Using the UNSW-NB15 Dataset en_US
dc.type Article en_US
dc.volume 8 en_US
dc.issue 5 en_US
dc.pagestart 478 en_US
dc.pageend 487 en_US
dc.contributor.authorcountry Morocco en_US
dc.contributor.authoraffiliation School of Science and Engineering, Al Akhawayn University in Ifrane, Ifrane 53000, Morocco en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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