Research on network intrusion detection based on SMOTEENN and improved CatBoost algorithm
Huoming Zhang, Yinhui Zhang, LU Ping-lan, Wang Cheng · 2023
To address the problems of network intrusion data imbalance and prediction accuracy, a network intrusion detection model based on SMOTEENN (Synthetic Minority Oversampling Technique Edited Nearest Neighbor) and improved CatBoost algorithm is proposed, and SMOTEENN sampling is used to solve the data imbalance problem from the data level. The original CatBoost algorithm loss function is replaced with focal loss function to enhance the classification performance of CatBoost algorithm from the level of sample classification difficulty. And the Bayesian optimization algorithm is used to realize the CatBoost hyperparameter combination seeking. The network intrusion dataset NSL-KDD (National Security Letter Knowledge Discovery in Database) is input to the improved model and compared with other similar algorithms. The results show that the proposed method in this paper is optimal in terms of accuracy, F1-sacore, and AP value, To provide new ideas for network intrusion algorithm research.