Network Anomaly Detection with Bayesian Self-Organizing Maps
| Autor: | Eduardo Miguel De la Hoz Correa, Emiro De la Hoz Franco, Andrés Ortiz García, Alberto Prieto Espinosa, Julio Ortega Lopera |
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| Rok vydání: | 2013 |
| Předmět: |
Self-organizing map
021110 strategic defence & security studies Artificial neural network business.industry Anomaly-based intrusion detection system Computer science 0211 other engineering and technologies Probabilistic logic 02 engineering and technology Intrusion detection system Machine learning computer.software_genre 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Artificial intelligence Data mining business computer Block (data storage) |
| Zdroj: | Advances in Computational Intelligence ISBN: 9783642386787 IWANN (1) |
| DOI: | 10.1007/978-3-642-38679-4_53 |
| Popis: | The growth of the Internet and consequently, the number of interconnected computers through a shared medium, has exposed a lot of relevant information to intruders and attackers. Firewalls aim to detect violations to a predefined rule set and usually block potentially dangerous incoming traffic. However, with the evolution of the attack techniques, it is more difficult to distinguish anomalies from the normal traffic. Different intrusion detection approaches have been proposed, including the use of artificial intelligence techniques such as neural networks. In this paper, we present a network anomaly detection technique based on Probabilistic Self-Organizing Maps (PSOM) to differentiate between normal and anomalous traffic. The detection capabilities of the proposed system can be modified without retraining the map, but only modifying the activation probabilities of the units. This deals with fast implementations of Intrusion Detection Systems (IDS) necessary to cope with current link bandwidths. |
| Databáze: | OpenAIRE |
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