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Título : A neural-visualization IDS for honeynet data
Autor : Herrero, Alvaro
Zurutuza, Urko
Corchado, Emilio
Publicado en: International Journal of Neural Systems. 2012, V. 22, n. 2, 1250005
Editorial : World Scientific Publishing
Fecha de publicación : abr-2012
ISSN : 0129-0657
DOI: 10.1142/S0129065712500050
Resumen : Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed
Palabras clave: Artificial Neural Networks
Unsupervised Learning
Projection Models
Network & Computer Security
Intrusion Detection
Honeypots
URI : http://hdl.handle.net/10259/3861
Versión del editor: http://www.worldscientific.com/doi/abs/10.1142/S0129065712500050
Aparece en las colecciones: Artículos GICAP

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