关键词: 入侵检测, 机器学习, 集成学习, 深度学习, 二分类, 多分类, UNSWNB15

Abstract: Nowadays, network threats are constantly evolving and demonstrate increasing invisibility. Studying the performance and characteristics of multiple machine learning models for intrusion detection on modern traffic data is of greater significance to improve the timeliness of intrusion detection systems. This paper explores the use of recent efficient machine learning models, including ensemble learning(Random Forest, XGBoost, LightGBM) and deep learning(CNN, LSTM, GRU, etc) models for intrusion detection tasks on the public dataset UNSWNB15.We elaborate the task flow and experimental configuration, compare and analyze the experimental results of different models, summarize the characteristics of each model in the network intrusion detection task. The experimental results demonstrate that, under a 10% sampled dataset of UNSWNB15, the bestperforming model for the binary classification task among the experimental models is LightGBM, with an F1 score of 0.897, an accuracy of 89.86%, a training time of 1.98s, and a prediction time of 0.11s. In the case of multiclassification tasks, the most comprehensive prediction model among the experimental models is XGBoost, with an overall F1 score of 0.7907, an accuracy of 75.96%, a training time of 144.79s, and a prediction time of 0.21s.

Key words: intrusion detection, machine learning, ensemble learning, deep learning, binary classification, multiclass classification, UNSWNB15