| Issue |
ITM Web Conf.
Volume 43, 2022
The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/itmconf/20224301003 | |
| Published online | 14 March 2022 | |
A Survey on Network Intrusion Detection using Convolutional Neural Network
1 Computer Engineering Dept. University of Sharjah, Sharjah, UAE
2 Computer Engineering Dept. University of Sharjah, Sharjah, UAE
3 Electrical & Computer Engineering Dept. University of Western Ontario, London, ON, Canada
U19200543@sharjah.ac.ae
anassif@sharjah.ac.ae
minjadat@uwo.ca
Nowadays Artificial Intelligence (AI) and studies dedicated to this field are gaining much attention worldwide. Although the growth of AI technology is perceived as a positive development for the industry, many factors are being threatened. One of these factors is security, especially network security. Intrusion Detection System (IDS) which provides real-time network security has been recognized as one of the most effective security solutions. Moreover, there are various types of Neural Networks (NN) approaches for IDS such as ANN, DNN, CNN, and RNN. This survey mainly focuses on the CNN approach, whether individually used or along with another technique. It analyses 81 articles that were carefully investigated based on a specific criterion. Accordingly, 28 hybrid approaches were identified in combination with CNN. Also, it recognized 21 evaluation metrics that were used to validate the models, as well as 12 datasets.
Key words: Convolutional neural network / Intrusion detection system / Network security
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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