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Título
Clustering and Neural Visualization for Flow-Based Intrusion Detection
Autor(es)
Materia
Computer Science
Fecha de publicación
2015/06
Editor
Springer Science + Business Media
Citación
International Joint Conference. CISIS’15 and ICEUTE’15. Advances in Intelligent Systems and Computing. Volumen 369, pp. 333-345.
Resumen
To secure a system, potential threats must be identified and therefore, attack features are understood and predicted. Present work aims at being one step towards the proposal of an Intrusion Detection System (IDS) that faces zero-day attacks. To do that, MObile VIsualisation Connectionist Agent-Based IDS (MOVICAB-IDS), previously proposed as a hybrid-intelligent visualization-based IDS, is being upgraded by adding clustering methods. To check the validity of the proposed clustering extension, it faces a realistic flow-based dataset in present paper. The analyzed data come from a honeypot directly connected to the Internet (thus ensuring attack-exposure) and is analyzed by clustering and neural tools, individually and in conjunction. Through the experimental stage, it is shown that the combination of clustering and neural projection improves the detection capability on a continuous network flow.
URI
ISBN
978-3-319-19712-8(Print) / 978-3-319-19713-5(Online)
ISSN
2194-5357(Print) / 2194-5365(Online)
Colecciones
- BISITE. Congresos [289]