Two-tier network anomaly detection model: a machine learning approach

Author:

Pajouh Hamed Haddad,Dastghaibyfard GholamHossein,Hashemi Sattar

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Reference28 articles.

1. Bouzida, Y., & Cuppens, F. (2006). Neural networks vs. decision trees for intrusion detection. In IEEE/IST Workshop on Monitoring, Attack Detection and Mitigation (MonAM), Tuebingen (pp. 28–29).

2. Chan, P.K., Mahoney, M.V., & Arshad, M.H. (2005). Learning Rules and Clusters for Anomaly Detection in Network Traffic. Managing Cyber Threats: Issues, Approaches and Challenges, 5, 81–99.

3. Chawla, N.V., Bowyer, K.W., Hall, L.O., & Kegelmeyer, W.P. (2011). SMOTE: synthetic minority over-sampling technique, arXiv: 11061813 .

4. Dua, S., & Du, X. (2011). Data Mining and Machine Learning in Cybersecurity. USA: CRC Press.

5. Friedman, J.H., Bentley, J.L., & Finkel, R.A. (1977). An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software TOMS, 3(3), 209–226.

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