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

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).

[1]  Sokratis K. Katsikas,et al.  Intrusion Detection Using Evolutionary Neural Networks , 2008, 2008 Panhellenic Conference on Informatics.

[2]  Seyed Mojtaba Hosseini Bamakan,et al.  A New Intrusion Detection Approach Using PSO based Multiple Criteria Linear Programming , 2015, ITQM.

[3]  Attia A. El-Fergany,et al.  Parameter extraction of photovoltaic generating units using multi-verse optimizer , 2016 .

[4]  Victor Valeriu Patriciu,et al.  Intrusions detection based on Support Vector Machine optimized with swarm intelligence , 2014, 2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI).

[5]  Hui Li,et al.  Evolutionary artificial neural networks: a review , 2011, Artificial Intelligence Review.

[6]  Sung-Bae Cho,et al.  Evolutionary neural networks for anomaly detection based on the behavior of a program , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[7]  Dipankar Dasgupta,et al.  An evolutionary approach to generate fuzzy anomaly (attack) signatures , 2003, IEEE Systems, Man and Cybernetics SocietyInformation Assurance Workshop, 2003..

[8]  Wolfgang Banzhaf,et al.  The use of computational intelligence in intrusion detection systems: A review , 2010, Appl. Soft Comput..

[9]  Risto Miikkulainen,et al.  Intrusion Detection with Neural Networks , 1997, NIPS.

[10]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[11]  Li Shuhui.  Improved Evolutionary Neural Network Algorithm and Its Applications in Intrusion Detection , 2010 .

[12]  Mehdi MORADI,et al.  A Neural Network Based System for Intrusion Detection and Classification of Attacks , 2004 .

[13]  Chih-Fong Tsai,et al.  CANN: An intrusion detection system based on combining cluster centers and nearest neighbors , 2015, Knowl. Based Syst..

[14]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

[15]  Mohiuddin Ahmed,et al.  A survey of network anomaly detection techniques , 2016, J. Netw. Comput. Appl..

[16]  Bernhard Sick,et al.  Evolutionary optimization of radial basis function networks for intrusion detection , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[17]  Nour Moustafa,et al.  UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) , 2015, 2015 Military Communications and Information Systems Conference (MilCIS).

[18]  Salvatore J. Stolfo,et al.  Adaptive Intrusion Detection: A Data Mining Approach , 2000, Artificial Intelligence Review.

[19]  Ian H. Witten,et al.  Chapter 1 – What's It All About? , 2011 .

[20]  Samaneh Rastegari,et al.  Intelligent network intrusion detection using an evolutionary computation approach , 2015 .

[21]  Mohammed El-Beltagy,et al.  Artificial Neural Networks Architecture For Intrusion Detection Systems and Classification of Attacks , 2007 .

[22]  Ge Yu,et al.  Method of evolutionary neural network-based intrusion detection , 2001, 2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No.01EX479).

[23]  Jian Li,et al.  The research and implementation of intelligent intrusion detection system based on artificial neural network , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[24]  Jill Slay,et al.  The Significant Features of the UNSW-NB15 and the KDD99 Data Sets for Network Intrusion Detection Systems , 2015, 2015 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS).

[25]  Jill Slay,et al.  The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set , 2016, Inf. Secur. J. A Glob. Perspect..