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AnIntelligentIntrusionDetectionSystemForHoynetData.pdf (2.235Mb)
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Microsoft Academic
Partekatu
Gorde erreferentzia
Mendely
Izenburua
A neural-visualization IDS for honeynet data
Egilea
Zurutuza, Urko ccMondragon Unibertsitatea
Egilea (beste erakunde batekoa)
Herrero, Álvaro
Corchado, Emilio
Ikerketa taldea
Análisis de datos y ciberseguridad
Argitalpen data
2012
Argitaratzailea
World Scientific
Gako-hitzak
Artificial Neural Networks
Unsupervised Learning
Projection Models
Network & Computer Security
Intrusion Detection
Honeypots
Laburpena
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
http://hdl.handle.net/20.500.11984/5586
Argitaratzailearen bertsioa
https://doi.org/10.1142/S0129065712500050
ISSN
0129-0657
Non argitaratua
International Journal of Neural Systems  Vol. 22. Nº. 2. Pp 121-128, 2012
Dokumentu-mota
Artikulua
Bertsioa
Preprinta
Eskubideak
© 2012 World Scientific
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Bildumak
  • Artikuluak - Ingeniaritza [332]

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