Enhancing Wireless Intrusion Detection Using Machine Learning Classification with Reduced Attribute Sets
作者： Razan Abdulhammed , Miad Faezipour , Abdelshakour Abuzneid , Ali Alessa
DOI: 10.1109/IWCMC.2018.8450479
关键词:
摘要: In cybersecurity, machine learning approaches can predict and detect threats before they result in major security incidents. The design perform ance of an effective based Intrusion Detection System (IDS) depends upon the selected attributes classifier model. This paper considers multi-class classification for Aegean Wi-Fi Dataset (AWID) where classes represent 17 types IEEE 802.11 MAC Layer attacks. proposed work extracts four attribute sets 32, 10, 7 5 attributes, resp ectfully. classifiers achieved high accuracy with minimum false positive rates, presented outperforms previous related terms number classes, accuracy. maximum 99.64% Random Forest supply test 99.99% usi ng 10-fold cross validation approach J48.
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