Reaching a consensus on access detection by a decision system

Author

Guevara, Cesar ; Santos, Marcos ; Martin, Jose Antonio ; Lopez, Victor

Author_Institution

Dept. Comput. Archit. & Autom. Control, Complutense Univ. of Madrid, Madrid, Spain

fYear

2014

fDate

16-18 May 2014

Firstpage

119

Lastpage

122

Abstract

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.

Keywords

Bayes methods; artificial intelligence; decision trees; security of data; IDS; Naive Bayes; UCS; access detection; artificial intelligence; classification techniques; classifier; computational tools; decision system; decision trees; heuristic methodologies; information security; intrusion detection; supervised classifying systems; Artificial intelligence; Classification algorithms; Computers; Databases; Decision trees; Intrusion detection; artificial intelligence; decision trees; heuristic methodologies; intrusion detection (IDS); naive Bayes; supervised classifying system UCS;

fLanguage

English

Publisher

ieee

Conference_Titel

Progress in Informatics and Computing (PIC), 2014 International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4799-2033-4

Type

conf

DOI

10.1109/PIC.2014.6972308

Filename

6972308