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Journal of Sensors
Volume 2018, Article ID 1578314, 9 pages
https://doi.org/10.1155/2018/1578314
Research Article

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

1School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
2China Information Technology Security Evaluation Center, Beijing 100085, China
3Department of Electronic and Information Engineering, Lanzhou Vocational Technical College, Lanzhou 730070, China

Correspondence should be addressed to Longjie Li; nc.ude.uzl@iljl

Received 17 August 2017; Revised 24 January 2018; Accepted 11 February 2018; Published 26 March 2018

Academic Editor: Eduard Llobet

Copyright © 2018 Longjie Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.