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Applied Intelligence

, Volume 48, Issue 8, pp 2315–2327 | Cite as

A new evolutionary neural networks based on intrusion detection systems using multiverse optimization

  • Ilyas Benmessahel
  • Kun Xie
  • Mouna Chellal
Article

Abstract

Building an intrusion detection system (IDS) has become an increasingly urgent issue for detecting network security breaches in computer and network systems. However, an effective and flexible IDS is imperative. In this paper, a new natural evolutionary algorithm (EA) called multiverse optimizer (MVO) is investigated and combined with an artificial neural network (ANN) to develop advanced detection approaches for an IDS. Under this context, the combination of ANN and EA produce evolutionary neural network (ENN). ENN makes this combination a new improved system for solving problems encountered by ANNs. The main idea of this work is to use an MVO to train a feed forward multilayer artificial neural network (MVO-ANN) to identify new attacks. This approach is applied to NSL-KDD and the new benchmark dataset called UNSW-NB15. In this manner, the effectiveness of our approach on detecting various forms of attack is demonstrated. Our results using UNSW-NB15 is better than those that were obtained using NSL-KDD. Furthermore, the efficacy of our proposed method is confirmed by performing better when compared to other well-known heuristic-based approaches such as practical swarm optimizer and artificial neural network (PSO-ANN).

Keywords

Multilayer feed forward (MLFF) Training neural network Evolutionary algorithm (EA) Multiverse optimization (MVO) Intrusion detection systems (IDSs) 

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of Computer Science and Electronics EngineeringHunan UniversityChangshaChina
  2. 2.School of Information Science and EngineeringCentral South UniversityChangshaChina

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