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

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 pot...

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Main Authors: Greensmith, Julie, Feyereisl, Jan, Aickelin, Uwe
Format: Article
Published: Evolutionary Intelligence 2008
Online Access:https://eprints.nottingham.ac.uk/988/
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author Greensmith, Julie
Feyereisl, Jan
Aickelin, Uwe
author_facet Greensmith, Julie
Feyereisl, Jan
Aickelin, Uwe
author_sort Greensmith, Julie
building Nottingham Research Data Repository
collection Online Access
description 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.
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spelling nottingham-9882020-05-04T20:27:44Z https://eprints.nottingham.ac.uk/988/ The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms Greensmith, Julie Feyereisl, Jan Aickelin, Uwe 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. Evolutionary Intelligence 2008 Article PeerReviewed Greensmith, Julie, Feyereisl, Jan and Aickelin, Uwe (2008) The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms. Evolutionary Intelligence, 1 (2). pp. 85-112. ISSN 1864-5909 http://www.springerlink.com/content/e36258777q212004/?p=2f9227646dc64d95bad7d60304ecf02c&pi=1 10.1007/s12065-008-0008-6 10.1007/s12065-008-0008-6 10.1007/s12065-008-0008-6
spellingShingle Greensmith, Julie
Feyereisl, Jan
Aickelin, Uwe
The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms
title The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms
title_full The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms
title_fullStr The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms
title_full_unstemmed The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms
title_short The DCA:SOMe comparison: a comparative study between two biologically-inspired algorithms
title_sort dca:some comparison: a comparative study between two biologically-inspired algorithms
url https://eprints.nottingham.ac.uk/988/
https://eprints.nottingham.ac.uk/988/
https://eprints.nottingham.ac.uk/988/