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Article

An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System

1
Centre for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
2
Centre for Cyber Security, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
3
Cybersecurity Malaysia, Level 7, Tower 1 Menara Cyber Axis Jalan Impact, Cyberjaya 63000, Malaysia
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(10), 1666; https://doi.org/10.3390/sym12101666
Received: 4 September 2020 / Revised: 16 September 2020 / Accepted: 24 September 2020 / Published: 13 October 2020
(This article belongs to the Section Computer Science and Symmetry/Asymmetry)
The significant increase in technology development over the internet makes network security a crucial issue. An intrusion detection system (IDS) shall be introduced to protect the networks from various attacks. Even with the increased amount of works in the IDS research, there is a lack of studies that analyze the available IDS datasets. Therefore, this study presents a comprehensive analysis of the relevance of the features in the KDD99 and UNSW-NB15 datasets. Three methods were employed: a rough-set theory (RST), a back-propagation neural network (BPNN), and a discrete variant of the cuttlefish algorithm (D-CFA). First, the dependency ratio between the features and the classes was calculated, using the RST. Second, each feature in the datasets became an input for the BPNN, to measure their ability for a classification task concerning each class. Third, a feature-selection process was carried out over multiple runs, to indicate the frequency of the selection of each feature. From the result, it indicated that some features in the KDD99 dataset could be used to achieve a classification accuracy above 84%. Moreover, a few features in both datasets were found to give a high contribution to increasing the classification’s performance. These features were present in a combination of features that resulted in high accuracy; the features were also frequently selected during the feature selection process. The findings of this study are anticipated to help the cybersecurity academics in creating a lightweight and accurate IDS model with a smaller number of features for the developing technologies. View Full-Text
Keywords: dataset analysis; features relevance; feature selections; neural network; classification; network security; metaheuristic algorithms; UNSW-NB15; KDD99 dataset analysis; features relevance; feature selections; neural network; classification; network security; metaheuristic algorithms; UNSW-NB15; KDD99
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MDPI and ACS Style

Al-Daweri, M.S.; Zainol Ariffin, K.A.; Abdullah, S.; Md. Senan, M.F.E. An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System. Symmetry 2020, 12, 1666. https://doi.org/10.3390/sym12101666

AMA Style

Al-Daweri MS, Zainol Ariffin KA, Abdullah S, Md. Senan MFE. An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System. Symmetry. 2020; 12(10):1666. https://doi.org/10.3390/sym12101666

Chicago/Turabian Style

Al-Daweri, Muataz S., Khairul A. Zainol Ariffin, Salwani Abdullah, and Mohamad F.E. Md. Senan. 2020. "An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System" Symmetry 12, no. 10: 1666. https://doi.org/10.3390/sym12101666

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