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• W2963786907 abstract "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 trade-off between accuracy and generalty. 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." @default.
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• W2963786907 date "2009-10-01" @default.
• W2963786907 modified "2023-10-07" @default.
• W2963786907 title "Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting" @default.
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