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- W3143388894 abstract "Characterizing network traffic with higher-dimensional features results in increased complexity of most detectors and classifiers for identifying traffic anomalies. Several key observations from existing studies confirm that network anomalies are typically distributed in a sparse way,with each anomaly essentially characterized by its lower-dimensional features. Based on this important finding,we exploit sparsity in designing a novel detection method for anomalies that ignores redundancies that are dynamically filtered from the feature sets and accurately classifies anomalies. Comparison of our method with three well known techniques shows a10% improvement in accuracy with an O(n) complexity of the classifier." @default.
- W3143388894 created "2021-04-13" @default.
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- W3143388894 date "2014-01-01" @default.
- W3143388894 modified "2023-09-28" @default.
- W3143388894 title "Uncovering network traffic anomalies based on their sparse distributions" @default.
- W3143388894 hasPublicationYear "2014" @default.
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