Sustainable Ensemble Learning Driving Intrusion Detection Model
Xinghua Li, Mengyao Zhu, Laurence T. Yang, Mengfan Xu, Zhuo Ma, Cheng Zhong, Hui Li, Yang Xiang
Abstract
Nowadays, in machine learning based intrusion detection systems, ensemble learning is a commonly adopted method to improve the detection accuracy. Unfortunately, the existing works have not considered the accumulation and reuse of historical knowledge, as well as the sensitivity of the detection model to different types of attacks, which leads to a low detection accuracy. To address the issue, this article proposes a model based on sustainable ensemble learning. In the model training stage, by taking the individual classifiers probability output and classification confidence as the training data, we build multi-class regression models such that ensemble learning adapts to different attacks. Besides, in the updating stage, an iterative updating method is presented, where the parameters and decision results of the historical model are added to the training process of the new ensemble model to realize the incremental learning. Experiment results show that the proposed model significantly outperforms the existing solutions in terms of detection accuracy, false alarm, stability and robustness.