IMPLEMENTATION OF NEURAL NETWORKS AND BOOSTING ALGORITHMS FOR EFFECTIVE INTRUSION DETECTION
DOI:
https://doi.org/10.47839/ijc.7.3.525Keywords:
Neural networks, intrusion detection, computer security, boosting algorithms, AdaBoostAbstract
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
Web Application Security Consortium. Classification of security threats. – Information on: www.webappsec.org.
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.
V. Golovko, P. Kachurka and L. Vaitsekhovich. Neural Network Ensembles for Intrusion Detection // In Proceedings of the 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS-2007) / Research Institute of Intelligent Computer Systems, Ternopil National Economic University and University of Applied Sciences Fachhochschule Dortmund – Dortmund, Germany, 2007. – P. 578-583.
V. Golovko, L. Vaitsekhovich, P. Kochurko and U. Rubanau. Dimensionality Reduction and Attack Recognition using Neural Network Approaches // Proceedings of the Joint Conference on Neural Networks (IJCNN 2007), Orlando, FL, USA – IEEE Computer Society, Orlando, 2007. – P. 2734-2739.
H. Drucker, R. Schapire and P. Simard. Improving performance in neural networks using a boosting algorithm // In S.J.Hanson, J.D.Cowan and C.L.Giles eds., Advanced in Neural Information Processing Systems 5, Denver, CO, Morgan Kaufmann, San Mateo, CA. – 1993. – P. 42-49.
Yoav Freund, Robert E. Schapire. A short introduction to boosting // Journal of Japanese Society for Artificial Intelligence. – 1999. – №14(5). – P. 771-780.
V. Golovko. Neural Networks: training, organization and application. Book 4: Tutorial for students / Edited by A. Galushkin. – M.: IPRJR, 2001. – P. 256.
V. Venkatachalam, S. Selvan. Performance comparison of intrusion detection system classifiers using various feature reduction techniques // I.J. of SIMULATION. – Vol. 9 No 1 – P. 30-39.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.