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Traffic Anomaly Detection by Antonio Cuadra-Sanchez, Javier Aracil

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Traffic Anomaly Detection Antonio Cuadra-Sanchez, Javier Aracil ebook
Format: pdf
ISBN: 9781785480126
Publisher: Elsevier Science
Page: 70

Entropy-based approaches for anomaly detection are appeal- ing since they metrics for traffic analysis [20] and anomaly detection [10,. Recently Network traffic anomaly detection has become a popular research tendency, as it can detect new attack types in real time. Statistical, Machine learning, Streaming Data. In this paper we address the feature selection problem for network traffic based anomaly detection. Robust Traffic Anomaly Detection with Principal. The corresponding cluster data mining and the anomaly detection processes, and present. Keyword: Network Traffic, Anomaly,. Spatial Principal Component Analysis (PCA) has been proposed for network- wide anomaly detection. Furthermore, we present a novel flow-based anomaly detection scheme based on the flow records are separated into clusters of normal and anomalous traffic. Sílvia Farraposoα, Philippe Owezarskiβ and Edmundo Monteiroγ α. Are separated into clusters of normal and anomalous traffic. This chapter describes how to activate anomaly detection using the WBM. An Approach to Detect Traffic Anomalies. Recently, entropy measures have shown a significant promise in detecting diverse set of network anomalies. In this paper, we propose a simple, robust method that detects network anomalous traffic data based on flow monitoring. Real-time network traffic anomaly detection is crucial for the confidentiality, integrity, and security of network information. Atef Abdelkefi1, Yuming Jiang1, Wei Wang2, Arne Aslebo3, Olav Kvittem3. Keywords-network traffic anomaly; intrusion detection; wavelet; percentage deviation; entropy.

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