Practical real-time intrusion detection using machine learning approaches

Author:

Sangkatsanee Phurivit,Wattanapongsakorn Naruemon,Charnsripinyo Chalermpol

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference27 articles.

1. C. Jirapummin, N. Wattanapongsakorn, J. Kanthamanon, Hybrid neural networks for intrusion detection system, in: The International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), Thailand, 2002, pp. 928–931.

2. W. Lee, S. Stolfo, K. Mok, Mining in a data-flow environment: experience in network intrusion detection, in: The 5th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’99), San Diego, 1999.

3. Z. Pan, S. Chen, G. Hu, D. Zhang, Hybrid neural network and C4.5 for misuse detection, in: The 2nd International Conference on Machine Learning and Cybernetics, China, 2003, pp.2463–2467.

4. M. Moradi, M. Zulkernine, A neural network based system for intrusion detection and classification of attacks, in: The IEEE International Conference on Advances in Intelligent Systems Theory and Applications, Luxembourg, 2004, pp. 148–153.

5. N. Ngamwitthayanon, N. Wattanapongsakorn, C. Charnsripinyo, D.W. Coit, Multi-stage network-based intrusion detection system using back propagation neural networks, in: Asian International Workshop on Advanced Reliability Modeling (AIWARM), Taiwan, 2008, pp. 609–619.

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