Intrusion detection model based on ensemble learning in big data network
Xiaogang Yuan, Jianxin Wan, Pengliang Yuan, Yipiao Chen · 2024
Big data network has the characteristics of large scale and diverse types of data. How to quickly capture and accurately judge the types of network attacks and effectively defend against illegal intrusions is a key problem to be solved in network security. As a key research direction in the field of machine learning, ensemble learning has higher detection accuracy than single classifier, and is widely used in anomaly intrusion detection. Aiming at the challenges brought by the feature redundancy and class imbalance of the high-dimensional massive data generated in the big data network, the ensemble learning is adopted to integrate the classifical algorithms such as random forest, XGBoost and LightGBM into the network intrusion detection, and determines an optimal model through tuning and comparison. The experimental results show that the accuracy rate of the fusion model proposed in this paper can reach more than 95%, and its performance is better than the optimal submodel. The fusion model method based on ensemble learning can detect and identify the intrusion data more effectively, which is of great significance for maintaining the security of cyberspace and ensuring the sustainable development of society.