Automatic feature selection for anomaly detection

Article, Contribution for newspaper or weekly magazine English OPEN
Marius Kloft; Ulf Brefeld; Patrick Düessel; Christian Gehl; Pavel Laskov;
  • Related identifiers: doi: 10.1145/1456377.1456395
  • Subject: Support vector data description | Multiple kernel learning | Business informatics | Network security | /dk/atira/pure/core/keywords/informatics | Anomaly detection | Intrusion detection | Informatics | /dk/atira/pure/core/keywords/547106742 | Machine learning | Feature selection

<p>A frequent problem in anomaly detection is to decide among different feature sets to be used. For example, various features are known in network intrusion detection based on packet headers, content byte streams or application level protocol parsing. A method for auto... View more
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