Improving the performance of neural networks with random forest in detecting network intrusions

Wenjuan Li, Yuxin Meng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Neural Networks such as RBFN and BPNN have been widely studied in the area of network intrusion detection, with the purpose of detecting a variety of network anomalies (e.g., worms, malware). In real-world applications, however, the performance of these neural networks is dynamic regarding the use of different datasets. One of the reasons is that there are some redundant features for the dataset. To mitigate this issue, in this paper, we propose an approach of combining Neural Networks with Random Forest to improve the accuracy of detecting network intrusions. In particular, we design an intelligent anomaly detection system that uses the algorithm of Random Forest in the process of feature selection and selects an appropriate algorithm in an adaptive way. In the evaluation, we conducted two major experiments using the KDD1999 dataset and a real dataset respectively. The experimental results indicate that Random Forest can enhance the performance of Neural Networks by identifying important and closely related features and that our developed system can select a better algorithm intelligently.

Original languageEnglish
Title of host publicationAdvances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings
PublisherSpringer Verlag
Pages622-629
Number of pages8
EditionPART 2
ISBN (Print)9783642390678
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event10th International Symposium on Neural Networks, ISNN 2013 - Dalian, China
Duration: 4 Jul 20136 Jul 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7952 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Symposium on Neural Networks, ISNN 2013
Country/TerritoryChina
CityDalian
Period4/07/136/07/13

Keywords

  • Anomaly Intrusion Detection
  • Intelligent Applications
  • Neural Networks
  • Random Forest

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this