Browse > Article

An Intrusion Detection System based on the Artificial Neural Network for Real Time Detection  

Kim, Tae Hee (동신대학교 에너지융합대학 융합정보보안전공)
Kang, Seung Ho (동신대학교 에너지융합대학 융합정보보안전공)
Publication Information
Convergence Security Journal / v.17, no.1, 2017 , pp. 31-38 More about this Journal
Abstract
As the cyber-attacks through the networks advance, it is difficult for the intrusion detection system based on the simple rules to detect the novel type of attacks such as Advanced Persistent Threat(APT) attack. At present, many types of research have been focused on the application of machine learning techniques to the intrusion detection system in order to detect previously unknown attacks. In the case of using the machine learning techniques, the performance of the intrusion detection system largely depends on the feature set which is used as an input to the system. Generally, more features increase the accuracy of the intrusion detection system whereas they cause a problem when fast responses are required owing to their large elapsed time. In this paper, we present a network intrusion detection system based on artificial neural network, which adopts a multi-objective genetic algorithm to satisfy the both requirements: accuracy, and fast response. The comparison between the proposing approach and previously proposed other approaches is conducted against NSL_KDD data set for the evaluation of the performance of the proposing approach.
Keywords
Intrusion Detection System; Artificial Neural Network; Multi-objective Genetic Algorithm; Feature selection; NSL_KDD data set;
Citations & Related Records
연도 인용수 순위
  • Reference
1 G. Wang, J. Hao, J. Ma, and L. Huang, "A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering", Expert Systems with Applications, Vol. 37, Issue 9, pp. 6225-6232, 2010.   DOI
2 Md. Al mohediHasan, M. Nasser, and B. Pal, "On the KDD'99 Dataset: Support Vector Machine Based Intrusion Detection System (IDS) with Different Kernels", International Journal of Electronics Communication and Computer Engineering, Vol. 4, Issue 4, pp. 1164-1170, 2013.
3 H. S. Huang, "Supervised feature selection: A tutorial", Artificial Intelligence Research, Vol. 4, No. 2, 2015.
4 H. G. Kayacik, A. N. Zincir-Heywood, and M. I. Heywood, "Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets," in Thrid Annual Conference on Privacy, Security and Trust, St. Andrews, New Brunswick, Canada, 2005.
5 A. A. Olusola, A. S. Oladele, and D. O. Abosede, "Analysis of KDD '99 Intrusion Detection Dataset for Selection of Relevance Features," in Proc. of the World Congress on Engineering and Computer Science, Vol. 1, 2010.
6 S. Parazad, E. Saboori, and A. Allahyar, "Fast Feature Reduction in Intrusion Detection Datasets," in MIPRO, Proceedings of the 35th International Convention, pp.1023-1029, 2012.
7 KDD Cup 1999. Available on:http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, 2007.
8 NSL_KDD data set. Avalilable on: http://nsl.cs.unb.ca/NSL-KDD/
9 M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, "A Detailed Analysis of the KDD CUP 99 Data Set," Proc. 2009 IEEE Int. Conf. Comput. Intell. Security Defense Appl. CISDA, pp. 53-58, 2009.
10 T. Naidoo, J. R. Tapamo and A. McDonald, "Feature selection for anomaly-based network intrusion detection using cluster validity indices", In: SATNAC: Africa - The Future Communications Galaxy, 2015.
11 M. Sabhnani and G. Serpen, "Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context," Proc. of International Conference on Machine Learning: Models, Technologies, and Applications, pp. 209-215, 2013.
12 A. Konak, D. Coit, and A. Smith, "Multi-objective optimization using genetic algorithms: a tutorial", Reliability Engineering & System Safety in Special Issue - Genetic Algorithms and Reliability, vol. 92, pp. 992-1007, 2006.
13 S. H. Kang, and K. J. Kim, "A feature selection approach to find optimal feature subsets for the network intrusion detection system", Cluster Computing, Vol. 19, Issue 1, pp 325-333, 2016.   DOI
14 I. S. Jeong, H. K. Kim, T. H. Kim, D. H. Lee, K. J. Kim and S. H. Kang, "A Feature Selection Approach Based on Simulated Annealing for Detecting Various Denial of Service Attacks", Convergence Security, Vol. 1, pp. 1-18., 2016.