Sajith, P.J. and Nagarajan, G. (2021) Optimized Intrusion Detection System Using Computational Intelligent Algorithm. In: Mallick P.K., Bhoi A.K., Chae G.S. and Kalita K., Eds., Advances in Electronics, Communication and Computing, Springer, Singapore, 633-639.
https://doi.org/10.1007/978-981-15-8752-8_64

has been cited by the following article:

• TITLE: Application of Weighted Cross-Entropy Loss Function in Intrusion Detection

  AUTHORS: Ziyun Zhou, Hong Huang, Binhao Fang

  KEYWORDS: Cross-Entropy Loss Function, Visualization Analysis, Intrusion Detection, KDD Data Set, Accuracy

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.11, November 8, 2021

  ABSTRACT: The deep learning model is overfitted and the accuracy of the test set is reduced when the deep learning model is trained in the network intrusion detection parameters, due to the traditional loss function convergence problem. Firstly, we utilize a network model architecture combining Gelu activation function and deep neural network; Secondly, the cross-entropy loss function is improved to a weighted cross entropy loss function, and at last it is applied to intrusion detection to improve the accuracy of intrusion detection. In order to compare the effect of the experiment, the KDDcup99 data set, which is commonly used in intrusion detection, is selected as the experimental data and use accuracy, precision, recall and F1-score as evaluation parameters. The experimental results show that the model using the weighted cross-entropy loss function combined with the Gelu activation function under the deep neural network architecture improves the evaluation parameters by about 2% compared with the ordinary cross-entropy loss function model. Experiments prove that the weighted cross-entropy loss function can enhance the model’s ability to discriminate samples.