IMPLEMENTATION OF NEURAL NETWORKS AND BOOSTING ALGORITHMS FOR EFFECTIVE INTRUSION DETECTION

Authors

  • Vladimir Golovko
  • Leanid Vaitsekhovich

DOI:

https://doi.org/10.47839/ijc.7.3.525

Keywords:

Neural networks, intrusion detection, computer security, boosting algorithms, AdaBoost

Abstract

In this article the classification task in the domain of intrusion detection is considered. Often a chosen algorithm is not good enough for practical use. So the question arises how is it possible to improve the performance? In this case we can employ so-called Committee Machines that increase accuracy and reliability of the base classification model. These advantages are the result of dividing complex computational problems among several experts. The knowledge of each expert influences on the general conclusion of Committee Machine.

References

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V. Golovko and L. Vaitsekhovich. Neural Network Techniques for Intrusion Detection // In Proceedings of the International Conference on Neural Networks and Artificial Intelligence (ICNNAI-2006) / Brest State Technical University – Brest, 2006. – P. 65-69.

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Published

2014-08-01

How to Cite

Golovko, V., & Vaitsekhovich, L. (2014). IMPLEMENTATION OF NEURAL NETWORKS AND BOOSTING ALGORITHMS FOR EFFECTIVE INTRUSION DETECTION. International Journal of Computing, 7(3), 66-71. https://doi.org/10.47839/ijc.7.3.525

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Section

Articles