| Title | Using Deep Learning Techniques for Network Intrusion Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Al-Emadi, S., Al-Mohannadi, A., Al-Senaid, F. |
| Conference Name | 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) |
| Keywords | CNN, composability, computer network security, convolutional neural nets, convolutional neural network, convolutional neural networks, cyber-security attacks, Deep Learning, deep learning techniques, feature extraction, intelligent network intrusion detection system, Intrusion detection, learning (artificial intelligence), machine learning, Metrics, network intrusion attacks, network intrusion detection, Network security, Neural Network, pubcrawl, recurrent neural nets, recurrent neural network, Recurrent neural networks, resilience, Resiliency, RNN |
| 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 evaluate the performance 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. |
| DOI | 10.1109/ICIoT48696.2020.9089524 |
| Citation Key | al-emadi_using_2020 |
Using Deep Learning Techniques for Network Intrusion Detection