Intrusion Detection with Neural Networks Based on Knowledge Extraction by Decision Tree
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 hardware 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.
KeywordsIntrusion detection Pattern recognition Behavioral profile Security Decision tree Neural networks
This work has been partially supported by the Ministry of Higher Education, Science, Technology and Innovation (SENESCYT) of the Government of the Republic of Ecuador under the scholarship “Convocatoria Abierta 2011 y 2012”.
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