Towards Data-Driven Network Intrusion Detection Systems: Features Dimensionality Reduction and Machine Learning

Towards Data-Driven Network Intrusion Detection Systems: Features Dimensionality Reduction and Machine Learning

Cyberattacks

machine learning

deep learning

ML models

Cyberattacks have increased in tandem with the exponential expansion of computer networks and network applications throughout the world. In this study, we evaluate and compare four features selection methods, seven classical machine learning algorithms, and the deep learning algorithm on one million random instances of CSE-CIC-IDS2018 big data set for network intrusions. The dataset was preprocessed and cleaned and all learning algorithms were trained on the original values of features. The feature selection methods highlighted the importance of features related to forwarding direction (FWD) and two flow measures (FLOW) in predicting the binary traffic type; benign or attack. Furthermore, the results revealed that whether models are trained on all features or the top 30 features selected by any of the four features selection techniques used in this experiment, there is no significant difference in model performance. Moreover, we may be able to train ML models on only four features and have them perform similarly to models trained on all data,which may result in preferable models in terms of complexity, explainability, and scale for deployment. Furthermore, by choosing four unanimity features instead of all traffic features, training time may be reduced from 10% to 50% of the training time on all features.

Maabreh, Majdi

Obeidat, Ibrahim

Abu Elsoud , Esraa

Alnajjar, Asma

Alzyoud, Rahaf

Darwish, Omar

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 14 (2022); pp. 123-135

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-07-26

Copyright (c) 2022 Ibrahim obeidat, Majdi Maabreh, esraa abu elsoud , asma alnajjar, rahaf alzyoud, omar darwish

https://creativecommons.org/licenses/by/4.0

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article