Intrusion Detection with Neural Networks Based on Knowledge Extraction by Decision Tree

Author:

Guevara César,Santos Matilde,López Victoria

Publisher

Springer International Publishing

Reference13 articles.

1. 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)

2. 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

3. 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)

4. 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)

5. 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

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