Feature Selection in UNSW-NB15 and KDDCUP’99 datasets

JANARTHANAN, Tharmini and ZARGARI, Shahrzad (2017). Feature Selection in UNSW-NB15 and KDDCUP’99 datasets. In: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE),. IEEE.

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Link to published version:: https://doi.org/10.1109/ISIE.2017.8001537


Machine learning and data mining techniques have been widely used in order to improve network intrusion detection in recent years. These techniques make it possible to automate anomaly detection in network traffics. One of the major problems that researchers are facing is the lack of published data available for research purposes. The KDD’99 dataset was used by researchers for over a decade even though this dataset was suffering from some reported shortcomings and it was criticized by few researchers. In 2009, Tavallaee M. et al. proposed a new dataset (NSL-KDD) extracted from the KDD’99 dataset in order to improve the dataset where it can be used for carrying out research in anomaly detection. The UNSW-NB15 dataset is the latest published dataset which was created in 2015 for research purposes in intrusion detection. This research is analysing the features included in the UNSW-NB15 dataset by employing machine learning techniques and exploring significant features (curse of high dimensionality) by which intrusion detection can be improved in network systems. Therefore, the existing irrelevant and redundant features are omitted from the dataset resulting not only faster training and testing process but also less resource consumption while maintaining high detection rates. A subset of features is proposed in this study and the findings are compared with the previous work in relation to features selection in the KDD’99 dataset.

Item Type: Book Section
Additional Information: Electronic ISSN: 2163-5145
Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
Identification Number: https://doi.org/10.1109/ISIE.2017.8001537
Depositing User: Shahrzad Zargari
Date Deposited: 19 Oct 2017 10:38
Last Modified: 18 Mar 2021 06:46
URI: https://shura.shu.ac.uk/id/eprint/15662

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