A Convolutional Neural Network With Feature Selection-Based Network Intrusion Detection
Nassima Chaibi, Baghdad Atmani, Mostéfa Mokaddem · International Journal of Applied Evolutionary Computation · 2022
This paper attempts to provide a demonstration the importance of the feature selection (FS) in the data mining filed for the optimization. The author’s aim to develop a Convolutional Neural Network (CNN) based Network Intrusion Detection System (NIDS). The CNN was trained using the NSL-KDD dataset. The approach is divided into two methodologies: the first one, is to apply the CNN to the NSL-KDD dataset without FS, in the second methodology: the Information Gain (IG), Grain Ratio (GR) and Correlation Attribute (CA) were applied as FS methods then the CNN is use to classify the intrusion. The performance is proven by comparing our results with other previous works. Our experimentation results show that CNN with FS has a good accuracy 99,72%, true positive rate: 99,29%, false positive rate: 0,18%. Thus, the CNN with FS has outperform the other methods. But the methods use in the FS phase don’t guarantee the use of the best subset or the optimal subset. As future orientation is to develop another method for FS which guarantee the selection of the best and the optimal relevant feature.