Machine Learning Techniques for Anomalies Detection and Classification

Author:

Abdel-Aziz Amira Sayed,Hassanien Aboul Ella,Azar Ahmad Taher,Hanafi Sanaa El-Ola

Publisher

Springer Berlin Heidelberg

Reference21 articles.

1. Murali, A., Roa, M.: A survey on intrusion detection approaches. In: First International Conference on Information and Communication Technologies, ICICT, pp. 233–240 (2005)

2. Garcia-Teodora, P., Díaz-Verdejo, J., Maciá–Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security 28(1-2), 18–28 (2009)

3. Aziz, A.S.A., Azar, A.T., Hassanien, A.E., Hanafi, S.E.O.: Continuous Features Discretizaion for Anomaly Intrusion Detectors Generation. In: WSC17 2012 Online Conference on Soft Computing in Industrial Applications (2012)

4. Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., Srivastava, J.: A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection. In: Proceedings of the Third SIAM International Conference on Data Mining, vol. 3, pp. 25–36. SIAM (2003)

5. Brown, D.J., Suckow, B., Wang, T.: A Survey of Intrusion Detection Systems. TU Vienna, Austria (2000)

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