Intelligent methods for intrusion detection in local area networks

Annotation: Review of intelligent methods for intrusion detection in local area networks is presented. Publically available datasets of intrusions are shortly described. A problem of imbalanced classes appointed and approach for batch training of a neural network intrusion classifier with imbalanced classes is presented. In computer simulation, it is shown that such approach helps to train on classes with small amount of examples by the cost of larger classes.

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