Título
A neural-visualization IDS for honeynet dataAutor-a
Versión
Preprint en revisión
Derechos
© 2012 World ScientificAcceso
Acceso abiertoVersión del editor
https://doi.org/10.1142/S0129065712500050Publicado en
International Journal of Neural Systems Vol. 22. Nº. 2. Pp 121-128, 2012Primera página
121Última página
128Editor
World ScientificPalabras clave
Artificial Neural Networks
Unsupervised Learning
Projection Models
Network & Computer Security ... [+]
Unsupervised Learning
Projection Models
Network & Computer Security ... [+]
Artificial Neural Networks
Unsupervised Learning
Projection Models
Network & Computer Security
Intrusion Detection
Honeypots [-]
Unsupervised Learning
Projection Models
Network & Computer Security
Intrusion Detection
Honeypots [-]
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 De ... [+]
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. [-]
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