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Abstract: Intrusion detection systems (IDSs) must be capable of detecting new and unknown attacks, or anomalies. We study the problem of building detection models for both pure anomaly detection and combined misuse and anomaly detection (i.e., detection of both known and unknown intrusions) . We propose an algorithm to generate artificial anomalies to coerce the inductive learner into discovering an accurate boundary between known classes (normal connections and known intrusions) and anomalies. ... (Update)
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BibTeX entry: (Update)
W. Fan, W. Lee, M. Miller, S. J. Stolfo, and P. K. Chan, Using artificial anomalies to detect unknown and known network intrusions. In Proceedings of the first IEEE International conference on Data Mining, 2001. http://citeseer.ist.psu.edu/fan01using.html More
@inproceedings{ fan01using,
author = "Wei Fan and Matthew Miller and Salvatore J. Stolfo and Wenke Lee and Philip K. Chan",
title = "Using Artificial Anomalies to Detect Unknown and Known Network Intrusions",
booktitle = "{ICDM}",
pages = "123-130",
year = "2001",
url = "citeseer.ist.psu.edu/fan01using.html" }
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