Network Intrusion Detection System using Neural Networks

• Heitor Neto Federal University of Lavras
• Celso Ávila José do Rosário Vellano University
• Wilian Lacerda Federal University of Lavras

DOI: https://doi.org/10.5753/sbsi.2017.6044

Abstract

This article aims to present the development of a Network Intrusion Detection System (NIDS), with real environment trac, using the technique of Artificial Neural Networks to classify trac as intrusion or normal. For the experiments, two databases were used: the network trac database provided by ISCX; and a test database created in real-world environment. The results obtained using the technique of Artificial Neural Networks, trained with the ISCX database, showed accuracy rates of around 90%, for the ISCX data itself, and 98% for the real environment test data. These results arm the feasibility of implementing the technique of Artificial Neural Networks to solve problems of classification of trac of computer networks.

Keywords: Detecção de intrusão, Redes Neurais Artificiais

References

I. Brilhante, J. A. Macedo, F. M. Nardini, R. Perego, and C. Renso. Tripbuilder: A tool for recommending sightseeing tours. In Advances in Information Retrieval, pages 771–774. Springer, 2014.

B. Burke. RESTful Java with Jax-RS. O’Reilly Media, Inc., 1st edition, 2009.

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226–231, 1996.

A. R. Façanha, W. Viana, and M. C. Pequeno. Estudo de interfaces acessíveis para usuários com deficiência visual em dispositivos móveis touch screen. In XVI Congreso Internacional de Informática Educativa (TISE 2011), Santiago Chile, 2011.

Z. Farzanyar and N. Cercone. Trip pattern mining using large scale geo-tagged photos. In Proceedings of the International Conference on Computer and Information Science and Technology, 2015.

C. M. Figueiredo and E. Nakamura. Computação móvel: Novas oportunidades e novos desafios. Belo Horizonte: Universidade Federal de Minas Gerais: 28p, 2003.

L. S. Kennedy and M. Naaman. Generating diverse and representative image search results for landmarks. In Proceedings of the 17th international conference on World Wide Web, pages 297–306. ACM, 2008.

S. Kisilevich, F. Mansmann, and D. Keim. P-dbscan: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. In Proceedings of the 1st international conference and exhibition on computing for geospatial research & application, page 38. ACM, 2010.

Y. A. Lacerda, J. M. da Silva, L. B. Marinho, et al. Phoca: An extensible service-oriented tool for photo clustering analysis. In 29th SBBD – Demos and Applications Session, pages 227–232, 2014.

Y. A. Lacerda, R. G. F. Feitosa, G. A. R. M. Esmeraldo, C. d. S. Baptista, and L. B. Marinho. Compass clustering: A new clustering method for detection of points of interest using personal collections of georeferenced and oriented photographs. In Proceedings of the 18th Brazilian symposium on Multimedia and the web, pages 281–288. ACM, 2012.

L. Richardson and S. Ruby. Restful Web Services. O’Reilly, first edition, 2007.

S. Wu and L. A. Adamic. Visually impaired users on an online social network. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 3133–3142. ACM, 2014.

Y. Yang, Z. Gong, et al. Identifying points of interest using heterogeneous features. ACM Transactions on Intelligent Systems and Technology (TIST), 5(4):68, 2015.