Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

作     者：Li, Longjie Yu, Yang Bai, Shenshen Cheng, Jianjun Chen, Xiaoyun 

作者机构：Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China China Informat Technol Secur Evaluat Ctr Beijing 100085 Peoples R China Lanzhou Vocat Tech Coll Dept Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China 

出 版 物：《JOURNAL OF SENSORS》 (传感器杂志)

年 卷 期：2018年第2018卷第3-26期

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)] 0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 

基　　金：National Natural Science Foundation of China Fundamental Research Funds for the Central Universities [lzujbky-2017-192] 

主　　题：Malware (Computer software) Computer systems Machine learning Computer algorithms Computer architecture Particle swarm optimization 

摘      要：In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT) algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods.