Network Intrusion Detection using a combination of artificial neural networks in a hierarchical manner

Document Type : Original Article


1 Department of Computer Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran

2 مربی گروه کامپیوتر، دانشگاه آزاد اسلامی تربت‌حیدریه، تربت حیدریه، ایران

3 دانش آموخته کارشناسی کامپیوتر، گروه مهندسی کامپیوتر، دانشگاه تربت‌حیدریه، تربت‌حیدریه، ایران


With the growth of information technology, network security is one of the major issues and a great challenge. Intrusion detection systems, are the main component of a secure network that detect the attacks which are not detected by firewalls. These systems have a huge load of data to analyze. Investigations show that many features are unhelpful or ineffective, so removing some of these redundant features from the feature set is a solution to reduce the amount of data and thus increase the speed of the detection system.  To improve the performance of the intrusion detection system it is essential to understand the optimal property set for all kinds of attacks. This research, in addition to presenting a method for intrusion detection based on combining neural networks, also introduces a method for extracting optimal features of the KDD CUP 99 dataset which is a standard dataset for testing computer networks intrusion detection methods.


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  • Receive Date: 09 February 2018
  • Revise Date: 30 May 2019
  • Accept Date: 10 January 2020
  • First Publish Date: 21 May 2020