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Reaching a Consensus on Access Detection by a Decision System



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Santos Peñas, Matilde and Guevara Maldonado, César Byron and López López, María Victoria and Martín, José Antonio (2014) Reaching a Consensus on Access Detection by a Decision System. PIC 2014 - Proceedings of 2014 IEEE International Conference on Progress in Informatics and Computing (697230). pp. 119-122.

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Resumen (otros idiomas)

Classification techniques based on Artificial Intelligence are computational tools that have been applied to detection of intrusions (IDS) with encouraging results. They are able to solve problems related to information security in an efficient way. The intrusion detection implies the use of huge amount of information. For this reason heuristic methodologies have been proposed. In this paper, decision trees, Naive Bayes, and supervised classifying systems UCS, are combined to improve the performance of a classifier. In order to validate the system, a scenario based on real data of the NSL-KDD99 dataset is used.

Item Type:Article
Palabras clave (otros idiomas):Artificial intelligence, Heuristic methodologies, intrusiondDetection (IDS), Decision trees, Supervised dlassifying system UCS; Naive Bayes
Subjects:Sciences > Computer science
ID Code:33210
Deposited On:21 Sep 2015 12:54
Last Modified:21 Sep 2015 13:13

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