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The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms

Greensmith, Julie; Feyereisl, Jan; Aickelin, Uwe

Authors

Jan Feyereisl jqf@cs.nott.ac.uk

Uwe Aickelin uwe.aickelin@nottingham.ac.uk



Abstract

The dendritic cell algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a ‘context aware’ detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a self-organizing map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique.

Citation

Greensmith, J., Feyereisl, J., & Aickelin, U. The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms. Evolutionary Intelligence, 1(2), doi:10.1007/s12065-008-0008-6

Journal Article Type Article
Deposit Date Nov 20, 2008
Journal Evolutionary Intelligence
Print ISSN 1864-5909
Electronic ISSN 1864-5909
Publisher Humana Press
Peer Reviewed Peer Reviewed
Volume 1
Issue 2
DOI https://doi.org/10.1007/s12065-008-0008-6
Public URL http://eprints.nottingham.ac.uk/id/eprint/988
Publisher URL http://www.springerlink.com/content/e36258777q212004/?p=2f9227646dc64d95bad7d60304ecf02c&pi=1
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information The original publication is available at www.springerlink.com

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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