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Using Deep Learning Techniques for Network Intrusion Detection

Abstract: In recent years, there has been a significant increase in network intrusion attacks which raises a great concern from the privacy and security aspects. Due to the advancement of the technology, cyber-security attacks are becoming very complex such that the current detection systems are not sufficient enough to address this issue. Therefore, an implementation of an intelligent and effective network intrusion detection system would be crucial to solve this problem. In this paper, we use deep learning techniques, namely, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to design an intelligent detection system which is able to detect different network intrusions. Additionally, we compute the accuracy of the proposed solution using different evaluation matrices and we present a comparison between the results of our proposed solution to find the best model for the network intrusion detection system.

Authors: Sara Al-Emadi, Aisha Al-Mohannadi, Felwa Al-Senaid

Conference: Submitted to 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) – IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT’20)

Accepted on 19th December 2019

You can find the full text on IEEE and an early draft here


Cite this paper via the following BibTeX:

@INPROCEEDINGS{9089524, author={S. {Al-Emadi} and A. {Al-Mohannadi} and F. {Al-Senaid}}, booktitle={2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT)}, title={Using Deep Learning Techniques for Network Intrusion Detection}, year={2020}, volume={}, number={}, pages={171-176},}

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