Abstract
Detection of intruders or unauthorized access to computers has always been critical when dealing with information systems, where security, integrity and privacy are key issues. Although more and more sophisticated and efficient detection strategies are being developed and implemented, both hardware and software, there is still the necessity of improving them to completely eradicate illegitimate access. The purpose of this paper is to show how soft computing techniques can be used to identify unauthorized access to computers. Advanced data analysis is first applied to obtain a qualitative approach to the data. Decision tree are used to obtain users’ behavior patterns. Neural networks are then chosen as classifiers to identify intrusion detection. The result obtained applying this combination of intelligent techniques on real data is encouraging.
References
Haq, N.F., Onik, A.R., Avishek, M., Hridoy, K., Rafni, M., Shah, F.M., Farid, D.M.: Application of machine learning approaches in intrusion detection system: a survey. Int. J. Adv. Res. Artif. Intell. 4(3), 9–18 (2015)
Ahmed, M., Pal, R., Hossain, M.M., Bikas, M.A. N., Hasa, M.K.: A comparative study on the currently existing intrusion detection systems. In: International Association of Computer Science and Information Technology-Spring Conference, 2009, IACSITSC 2009, pp. 151–154. IEEE, April 2009
Guevara, C.B., Santos, M., López, M.V.: Negative selection and knuth morris pratt algorithm for anomaly detection. IEEE Lat. Am. Trans. 14(3), 1473–1479 (2016)
Jo, S., Sung, H., Ahn, B.: A comparative study on the performance of intrusion detection using Decision Tree and Artificial Neural Network models. J. Korea Soc. Digit. Ind. Inf. Manag. 11(4), 33–45 (2015)
Esmaily, J., Moradinezhad, R., Ghasemi, J.: Intrusion detection system based on Multi-Layer Perceptron Neural Networks and Decision Tree. In: 2015 7th Conference on Information and Knowledge Technology (IKT), pp. 1–5. IEEE, May 2015
Chen, Y., Abraham, A., Yang, B.: Hybrid flexible neural tree based intrusion detection systems. Int. J. Intell. Syst. 22(4), 337–352 (2007)
Liu, G., Yi, Z., Yang, S.: A hierarchical intrusion detection model based on the PCA neural networks. Neurocomputing 70(7), 1561–1568 (2007)
Amudhavel, J., Brindha, V., Anantharaj, B., Karthikeyan, P., Bhuvaneswari, B., Vasanthi, M., Vinodha, D.: A survey on intrusion detection system: state of the art review. Indian J. Sci. Technol. 9(11), 1–9 (2016)
Thomsen, E.: OLAP Solutions: Building Multidimensional Information Systems. John Wiley & Sons, New York (2002)
Prakash, P.O., Jaya, A.: Analyzing and predicting user behavior pattern from weblogs. Int. J. Appl. Eng. Res. 11(9), 6278–6283 (2016)
Guevara, C., Santos, M., López, V.: Data leakage detection algorithm based on sequences of activities. In: Proceedings of the 17th International Symposium Research in Attacks, Intrusions and Defenses RAID, vol. 8688, pp. 477–478. Springer, August 2014
Santos, M.: An applied approach to intelligent control. Revista Iberoamericana de Automática e Informática Industrial RIAI 8(4), 283–296 (2011)
Aburomman, A.A., Reaz, M.B.I.: A novel SVM-kNN-PSO ensemble method for intrusion detection system. Appl. Soft Comput. 38, 360–372 (2016)
Acknowledgments
This work has been partially supported by the Ministry of Higher Education, Science, Technology and Innovation (SENESCYT) of the Government of the Republic of Ecuador under the scholarship “Convocatoria Abierta 2011 y 2012”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Guevara, C., Santos, M., López, V. (2017). Intrusion Detection with Neural Networks Based on Knowledge Extraction by Decision Tree. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-47364-2_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47363-5
Online ISBN: 978-3-319-47364-2
eBook Packages: EngineeringEngineering (R0)