Intrusion Detection with Neural Networks Based on Knowledge Extraction by Decision Tree
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[1] Universidad Complutense de Madrid
Universidad Complutense de Madrid
Madrid, España
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- Localización: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings / coord. por Manuel Graña Romay, José Manuel López Guede, Oier Etxaniz, Álvaro Herrero Cosío, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2017, ISBN 978-3-319-47364-2, págs. 508-517
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Detection of intruders or unauthorized access to computers has always been critical when dealing with information systems, where security, integrity and privacy are key issues. Although more and more sophisticated and efficient detection strategies are being developed and implemented, both hard-ware and software, there is still the necessity of improving them to completely eradicate illegitimate access. The purpose of this paper is to show how soft computing techniques can be used to identify unauthorized access to computers. Advanced data analysis is first applied to obtain a qualitative approach to the data. Decision tree are used to obtain users’ behavior patterns. Neural networks are then chosen as classifiers to identify intrusion detection. The result obtained applying this combination of intelligent techniques on real data is encouraging.
