Abstract
With the advancement of computers and its computational enhancement over several decades of use, but with the growth in the dependencies and use of these systems, more and more concerns over the risk and security issues in networks have raised. In this paper, we have proposed approach using particle swarm optimization to optimize ART. Adaptive resonance theory is one of the most well-known machine-learning-based unsupervised neural networks, which can efficiently handle high-dimensional dataset. PSO on the other hand is a swarm intelligence-based algorithm, efficient in nonlinear optimization problem and easy to implement. The method is based on anomaly detection as it can also detect unknown attack types. PSO is used to optimize vigilance parameter of ART-1 and to classify network data into attack or normal. KDD ’99 (knowledge discovery and data mining) dataset has been used for this purpose.
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References
Eldos, T.M., Almazyad, A.S.: Adaptive resonance theory training parameters: pretty good sets. J. Comput. Sci. 6, 1443–1449 (2010). https://doi.org/10.3844/jcssp.2010.1438.1444
Prothives, K., Srinoy, S.: Member, IAENG. “Integrating ART and rough set approach for computer security” In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, IMECS 2009, vol. I. Hong Kong, March 18–20 (2009)
Granger, E., Henniges, P., Sabourin, R., Oliveira, L.S.: Supervised learning of fuzzy artmap neural networks through particle swarm optimization. J. Pattern Recogn. Res. 2(1), 27–60 (2007)
Al-Natsheh, H.T., Eldos, T.M.: Performance optimization of adaptive resonance neural networks using genetic algorithms. In: Foundations of computational intelligence, 2007. IEEE Symposium, 143–148 (2007)
Amini, M., Jalili, R., Shahriari, H.R.: RT-UNNID: a practical solution to real-time network-based intrusion detection using unsupervised neural networks. Elsevier Comput. Secur. J. 25(6) (2006)
Amini, M., Jalili, R.: Network-based intrusion detection using unsupervised adaptive resonance theory (ART). In: Proceedings of the Fourth Conference on Engineering of Intelligent Systems (EIS 2004). Madeira, Portugal (2004)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Joint Conference on Neural Networks, p. 1942e1948 (1995)
Carpenter, G.A., Grossberg, S.: Adaptive resonance theory. In: Encyclopedia of Machine Learning, Springer US, 22–35 (2010)
KDD’99 dataset, University of California, Irvine (1999). http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the kddcup 99 data set (2009) http://nsl.cs.unb.ca/NSL-KDD/
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Satpute, K., Kumar, R. (2018). Optimization of Adaptive Resonance Theory Neural Network Using Particle Swarm Optimization Technique. In: Reddy Edla, D., Lingras, P., Venkatanareshbabu K. (eds) Advances in Machine Learning and Data Science. Advances in Intelligent Systems and Computing, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-10-8569-7_1
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DOI: https://doi.org/10.1007/978-981-10-8569-7_1
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