UNADA: Unsupervised Network Anomaly Detection Using Sub-space Outliers Ranking

@inproceedings{Casas2011UNADAUN,
  title={UNADA: Unsupervised Network Anomaly Detection Using Sub-space Outliers Ranking},
  author={Pedro Casas and Johan Mazel and Philippe Owezarski},
  booktitle={Networking},
  year={2011}
}
Current network monitoring systems rely strongly on signature-based and supervised-learning-based detection methods to hunt out network attacks and anomalies. Despite being opposite in nature, both approaches share a common downside: they require the knowledge provided by an expert system, either in terms of anomaly signatures, or as normal-operation profiles. In a diametrically opposite perspective we introduce UNADA, an Unsupervised Network Anomaly Detection Algorithm for knowledge… CONTINUE READING
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