An AdamW-Based Deep Neural Network Using Feature Selection and Data Oversampling for Intrusion Detection
Zhuoer Lu, Xiaoyong Li, Pengfei Qiu · 2023
With the development of the Internet and the increasing number of Internet users, cyber security has become a major concern for most netizens. In this paper, we propose an AdamW-based neural network using feature selection and data oversampling for intrusion detection. First, we use the Random Forest classifier to select the 25 most important features for classifying network traffic. Second, given the imbalance of different types of samples in the NSL-KDD dataset, we use ADASYN oversampling to oversample the minority samples. In addition, to achieve better performance, we use AdamW as the optimizer of our deep neural network. Finally, we tune the hyperparameters of our deep neural network to get the best classification results for intrusion detection. Compared with other classical machine learning models for intrusion detection, our deep neural network achieves high intrusion detection performance: the test set loss is reduced to 0.0001 and the test set accuracy is improved to 99.8%.