A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

作     者：Wu Zhijun Li Yuqi Yue Meng 

作者机构：School of Safety Science and EngineeringCivil Aviation University of ChinaTianjin 300300China School of Electronic Information and AutomationCivil Aviation University of ChinaTianjin 300300China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2024年第21卷第11期

页      面：180-189页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 0839[工学-网络空间安全] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by the National Key R&D Program of China(No.2022YFB3904503) National Natural Science Foundation of China(No.62172418) 

主　　题：AdaBoost CNN detection rate false positive rate feature extraction intrusion detection 

摘      要：To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection ***,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost *** algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack *** AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data *** adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm *** results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.