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- W207004862 abstract "Regular anomaly detection approaches require the full network payload data or low-level access to thesystem. In cases where this kind of information is not available because of limited system access,encrypted data or privacy reasons these approaches cannot be used. We present an anomaly detectiontechnique for these cases using an outlier detection algorithm. The individual requests from a request logare grouped together to reconstruct the original sessions. These sessions form a new dataset from whichanomalies can be detected using a Self-Organizing Map. We train the Self-Organizing Map with a subsetof the sessions and then perform the outlier detection on the rest of the dataset. Using this approach weare able to identify several automated attacks, however the lack of information in the individual requestsmake it hard to distinguish regular user behavior from manually crafted attacks" @default.
- W207004862 created "2016-06-24" @default.
- W207004862 creator A5049006986 @default.
- W207004862 date "2012-06-15" @default.
- W207004862 modified "2023-09-26" @default.
- W207004862 title "detecting outliers in web-based network traffic" @default.
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