Hierarchical PCA-Based Multivariate Statistical Network Monitoring for Anomaly Detection
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Gabriel Maciá-Fernández; José Camacho; Pedro García-Teodoro; Rafael A. Rodríguez-Gómez
- Abstract:
- Multivariate Statistical Network Monitoring
(MSNM) is a methodology that leverages PCA processing of
information to provide insight on multiple variables evolution,
raising very good detection results that outperforms other
current methods. Regretfully, as any other detection approach,
it imposes a considerable burden due to the need to transfer
traffic-related data. In this paper, we suggest a hierarchical
approach for MSNM with two main benefits: it minimizes the
amount of data to be transferred through the network, and it
provides privacy capabilities. We test the feasibility as well as the
detection performance of the proposal within an experimental
environment, obtaining detection results that are similar to
non-hierarchical MSNM, but exhibiting a considerable reduction
in the amount of information sent through the network.
- Research areas:
- Year:
- 2016
- Type of Publication:
- In Proceedings
- Book title:
- 8th IEEE International Workshop on Information Forensics and Security (WIFS), Abu Dhabi (UAE)
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