Multiple classification algorithm based on ensemble learning for intrusion detection

作     者：Liu, Fulai Yue, Jiaqi Hu, Zhongyi Du, Ruiyan 

作者机构：Northeastern Univ Qinhuangdao Hebei Key Lab Marine Percept Network & Data Proc Lab Cognit Radio & Big Spectrum Data Proc Qinhuangdao 066004 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China 

出 版 物：《WIRELESS NETWORKS》 (Wireless Networks)

年 卷 期：2025年第31卷第3期

页      面：2143-2154页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Natural Science Foundation of Hebei Province [F2024501005] 

主　　题：Ensemble learning Intrusion detection Long short term memory Graph convolution neural network Support vector machine 

摘      要：As one of the promising technologies for enhancing network security, network intrusion detection is crucial for accurately identifying various network attacks. To advance the intrusion detection capability, a multiple classification ensemble learning algorithm (MCELID) is proposed by analyzing intrusion data from diverse perspectives. Firstly, an individual learner combining graph convolutional networks and long short-term memory networks (GCN-LSTM) is constructed to extract both structural information and temporal correlations from intrusion data. Meanwhile, a weighted support vector machine (W-SVM) model is employed for handling multi-classification tasks, where the conditional probability is calculated to derive the probability vector of input samples. Finally, the intrusion detection result is determined by a soft voting mechanism that combines the classification results from the GCN-LSTM and W-SVM models. Extensive experiments conducted on the KDD-CUP 99 dataset demonstrate that the proposed MCELID algorithm outperforms other existing methods in terms of detection accuracy, particularly in the recognition of DOS and Probe attacks.