Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting

Details Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting

2009-11-03

arxiv.org/abs/0911.0485v1

International Journal of Computer Science and Information Security, IJCSIS, Vol. 6, No. 1, pp. 083-091, October 2009, USA

Computer Science

Neural and Evolutionary Computing

9 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact F

Scientific paper

This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.

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