Enhancing intrusion detection with feature selection and neural network

Chunhui WU, Wenjuan LI

Research output: Contribution to journalArticlespeer-review

39 Citations (Scopus)


Intrusion detection systems are widely implemented to protect computer networks from threats. To identify unknown attacks, many machine learning algorithms like neural networks have been explored for anomaly based detection. However, in real-world applications, the performance of classifiers might be fluctuant with different data sets, while one main reason is due to some redundant or ineffective features. To mitigate this issue, this study investigates some feature selection methods and introduces an ensemble of Neural Networks and Random Forest to improve the detection performance. In particular, we design an intelligent system that can choose an appropriate algorithm in an adaptive way. In the evaluation, we study the feasibility of our approach with KDD99 data set and evaluate its practical performance with a real data set collected from a Honeynet environment. The experimental results indicate that as compared with similar approaches, our approach can overall provide a better result, through identifying important and closely related features. Copyright © 2021 Wiley Periodicals LLC.

Original languageEnglish
Pages (from-to)3087-3105
JournalInternational Journal of Intelligent Systems
Issue number7
Early online dateFeb 2021
Publication statusPublished - Jul 2021


Wu, C., & Li, W. (2021). Enhancing intrusion detection with feature selection and neural network. International Journal of Intelligent Systems, 36(7), 3087-3105. https://doi.org/10.1002/int.22397


  • Anomaly detection
  • Feature selection
  • Honeynet network
  • Neural networks
  • Random forest


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