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Title
A neural-visualization IDS for honeynet data
Author
Zurutuza, Urko ccMondragon Unibertsitatea
Author (from another institution)
Herrero, Álvaro
Corchado, Emilio
Research Group
Análisis de datos y ciberseguridad
Published Date
2012
Publisher
World Scientific
Keywords
Artificial Neural Networks
Unsupervised Learning
Projection Models
Network & Computer Security ... [+]
Artificial Neural Networks
Unsupervised Learning
Projection Models
Network & Computer Security
Intrusion Detection
Honeypots [-]
Abstract
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. [-]
URI
https://hdl.handle.net/20.500.11984/5586
Publisher’s version
https://doi.org/10.1142/S0129065712500050
ISSN
0129-0657
Published at
International Journal of Neural Systems  Vol. 22. Nº. 2. Pp 121-128, 2012
Document type
Article
Version
Submitted
Rights
© 2012 World Scientific
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Open Access
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  • Articles - Engineering [483]

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