| Título: | Anomaly detection using prior knowledge: application to TCP/IP traffic |
| Autores: |
Couchet, Jorge Ferreira, Enrique Manrique, Daniel Carrascal, Alberto |
| Fecha: |
2012-11-08 2006-08 2006-08 |
| Publicador: | Unversidad Nacional de La Plata |
| Fuente: |
 |
| Tipo: |
Objeto de conferencia Objeto de conferencia |
| Tema: |
intrusion detection false positive rates self-organizing maps Internet (e.g., TCP/IP) Architectures Ciencias Informáticas |
| Descripción: |
This article introduces an approach to anomaly intrusion detection based on a combination of supervised and unsupervised machine learning algorithms. The main objective of this work is an effective modeling of the TCP/IP network traffic of an organization that allows the detection of anomalies with an efficient percentage of false positives for a production environment. The architecture proposed uses a hierarchy of Self-Organizing Maps for traffic modeling combined with Learning Vector Quantization techniques to ultimately classify network packets. The architecture is developed using the known SNORT intrusion detection system to preprocess network traffic. In comparison to other techniques, results obtained in this work show that acceptable levels of compromise between attack detection and false positive rates can be achieved. IFIP International Conference on Artificial Intelligence in Theory and Practice - Neural Nets |
| Idioma: | Inglés |
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