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Applying Artificial Intelligence Methods to Network Attack Detection

  • Alexander Branitskiy
  • Igor KotenkoEmail author
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 151)

Abstract

This chapter reveals the methods of artificial intelligence and their application for detecting network attacks. Particular attention is paid to the representation of models based on neural, fuzzy, and evolutionary computations. The main object is a binary classifier, which is designed to match each input object to one of two sets of classes. Various schemes for combining binary classifiers are considered, which allows building models trained on different subsamples. Several optimizing techniques are proposed, both in terms of parallelization (for increasing the speed of training) and usage of aggregating compositions (for enhancing the classification accuracy). Principal component analysis is also considered, which is aimed at reducing the dimensionality of the analyzed attack feature vectors. A sliding window method was developed and adopted to decrease the number of false positives. Finally, the model efficiency indicators obtained during the experiments using the multifold cross-validation are provided.

Keywords

Detect Network Attacks Aggregate Composition Neuro-fuzzy Network Incorrect Classification Rate Normal Behavior Patterns 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research was supported by the Russian Science Foundation under grant number 18-11-00302.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.St. Petersburg Institute for Informatics and Automation of the Russian Academy of SciencesSt. PetersburgRussia

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