METHODS OF USING THE NEURAL NETWORK TO DETECT NEW TYPES OF NETWORK ATTACKS
The report discusses methods for tuning the hyperparameters of an artificial neural network in a system for detecting and classifying network intrusions. Assuming that the surface of the multidimensional space of hyperparameters is convex, an algorithm is proposed that selects the optimal set of hyperparameters in the search space according to the criterion of maximum accuracy of network intrusion classification. As a result of experiments using three different network intrusion detection data sets – KDDCup 99, NSL-KDD and UNSW-NB15 - the optimal hyperparameters of the MLP neural network were found. It is shown that the proposed method for automatic tuning of neural network hyperparameters makes it possible to achieve high intrusion detection results even on the simplest neural network under the condition of low computational costs. These results are not inferior to the results of modern models, where the hyperparameters were manually selected by the researchers.
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