Derechos de autor 2017 INVESTIGACION E INNOVACION EN INGENIERIAS
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Modelo de detección de intrusiones en sistemas computacionales, realizando selección de características con chi square, entrenamiento y clasificación con ghsom
Corresponding Author(s) : Johan Mardini
Investigación e Innovación en Ingenierías,
Vol. 5 Núm. 1 (2017): Enero - junio
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- Russell & Gangemi. Computer Security Basics, 1991
- R. Feiertag, C. Kahn, P. Porras, D. Schackenberg, S. Staniford – Chen and B. Tung. A common instrusión specification language, 1999
- M. Piattini & E. peso, Auditoría informática: un enfoque práctico, 2001
- A. Howard. Badland Morphology and Evolution: Interpretation Using a Simulation Model, 1997.
- D.J. Marchette. Computer Intrusion Detection and Network Monitoring: A Statistical. New York: Viewpoint Springer – Verlag, 2001.
- M. Sabhnani and G. Serpent, “Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context,” in Proc the International Conf. on Machine Learning: Models, Technologies, and Applications, Las Vegas, vol. 1, pp. 209-215, 2003.
- S. T. Brugger, and J. Chow. An assessment of the DARPA IDS Evaluation Dataset using Snort. Technical Report CSE-2007-1, University of California, Davis, Department of Computer Science, 2007.
- P. Dokas, L. Ertoz, V. Kumar, A. Lazarevic, J. Srivastava, and P. Tan. “Data mining for network intrusion detection”. NSF Workshop on Next Generation Data Mining, 2002.
- R. Bace & P. Mell. Intrusion detection systems. NIST special publication in intrusión detection systems.
- J. Andersen, S. Glasdam y D. Larsen. “New Concepts of Quality Assurance in Analytical Chemistry: Will They Influence the Way We Conduct Science in General?”. Chemical Engineering Communications, Vol. 203, No. 12, p. 1582-1590, 2016.
- H. Hota, and A.K. Shrivas. “Data mining approach for developing various models based on types of attack and feature selection as intrusión detection systems (IDS)”. Intelligent Computing, networking, and informatics, pp. 845-851, 2014.
- A. F. Namik & Z. A. Othman. Reducing network intrusion detection association rules using Chi-Squared pruning technique. Conference on Data Mining and Optimization, 2011.
- E. Pampalk, A. Rauber, D. Merkl. “Content-based organization and visualization of music archives”, 2002.
- D. Merkl, M. Dittenbach, A. Rauber. The Growing Hierarchical Self – Organizing Map. In: S. AMARL et al. (Eds): Proceedings of the International Joint Conference on Neural Networks, 2000.
- M. Dittenbach, D. Merkl, and A. Rauber. The Growing Hierarchical Self-Organizing Map. In Amari, S., Giles, C. L., Gori, M., and Puri, V., editors, Proc of the International Joint Conference on Neural Networks (IJCNN 2000), volume VI, pages 15 – 19, Como, Italy. IEEE Computer Society, 2000.
Referencias
Russell & Gangemi. Computer Security Basics, 1991
R. Feiertag, C. Kahn, P. Porras, D. Schackenberg, S. Staniford – Chen and B. Tung. A common instrusión specification language, 1999
M. Piattini & E. peso, Auditoría informática: un enfoque práctico, 2001
A. Howard. Badland Morphology and Evolution: Interpretation Using a Simulation Model, 1997.
D.J. Marchette. Computer Intrusion Detection and Network Monitoring: A Statistical. New York: Viewpoint Springer – Verlag, 2001.
M. Sabhnani and G. Serpent, “Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context,” in Proc the International Conf. on Machine Learning: Models, Technologies, and Applications, Las Vegas, vol. 1, pp. 209-215, 2003.
S. T. Brugger, and J. Chow. An assessment of the DARPA IDS Evaluation Dataset using Snort. Technical Report CSE-2007-1, University of California, Davis, Department of Computer Science, 2007.
P. Dokas, L. Ertoz, V. Kumar, A. Lazarevic, J. Srivastava, and P. Tan. “Data mining for network intrusion detection”. NSF Workshop on Next Generation Data Mining, 2002.
R. Bace & P. Mell. Intrusion detection systems. NIST special publication in intrusión detection systems.
J. Andersen, S. Glasdam y D. Larsen. “New Concepts of Quality Assurance in Analytical Chemistry: Will They Influence the Way We Conduct Science in General?”. Chemical Engineering Communications, Vol. 203, No. 12, p. 1582-1590, 2016.
H. Hota, and A.K. Shrivas. “Data mining approach for developing various models based on types of attack and feature selection as intrusión detection systems (IDS)”. Intelligent Computing, networking, and informatics, pp. 845-851, 2014.
A. F. Namik & Z. A. Othman. Reducing network intrusion detection association rules using Chi-Squared pruning technique. Conference on Data Mining and Optimization, 2011.
E. Pampalk, A. Rauber, D. Merkl. “Content-based organization and visualization of music archives”, 2002.
D. Merkl, M. Dittenbach, A. Rauber. The Growing Hierarchical Self – Organizing Map. In: S. AMARL et al. (Eds): Proceedings of the International Joint Conference on Neural Networks, 2000.
M. Dittenbach, D. Merkl, and A. Rauber. The Growing Hierarchical Self-Organizing Map. In Amari, S., Giles, C. L., Gori, M., and Puri, V., editors, Proc of the International Joint Conference on Neural Networks (IJCNN 2000), volume VI, pages 15 – 19, Como, Italy. IEEE Computer Society, 2000.