Foundations of r-contiguous Matching in Negative Selection for Anomaly Detection
Negative selection and the associated r-contiguous matching rule is a popular immune-inspired method for anomaly detection problems. In recent years, however, problems such as scalability and high false positive rate have been empirically noticed. In this article, negative selection and the associated r-contiguous matching rule are investigated from a pattern classification perspective. This includes insights in the generalization capability of negative selection and the computational complexity of finding r-contiguous detectors.
Foundations of r-contiguous Matching in Negative Selection for Anomaly Detection
Natural Computing
| Authors: | Thomas Stibor |
| Year/month: | 2009/9 |
| Pages: | 613-641 |
| Fulltext: | click here |
Abstract |
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| Negative selection and the associated r-contiguous matching rule is a popular immune-inspired method for anomaly detection problems. In recent years, however, problems such as scalability and high false positive rate have been empirically noticed. In this article, negative selection and the associated r-contiguous matching rule are investigated from a pattern classification perspective. This includes insights in the generalization capability of negative selection and the computational complexity of finding r-contiguous detectors. | |
Bibtex:
@article { stibor:2009,author = { Thomas Stibor},
title = { Foundations of r-contiguous Matching in Negative Selection for Anomaly Detection },
journal = { Natural Computing },
year = { 2009 },
month = { September },
pages = { 613-641 },
url = { https://link.springer.com/article/10.1007/s11047-008-9097-5 },
}