  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.
