The receptor density algorithm

Nick D.L. Owens; Andrew Greensted; Jon Timmis; Andy Tyrrell

doi:10.1016/j.tcs.2012.10.057

The receptor density algorithm

Nick D.L. Owens^*, Andrew Greensted, Jon Timmis, Andy Tyrrell

^*Corresponding author for this work

Department of Computer Science

University of York

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

This paper describes the biological and theoretical foundations of a new Artificial Immune System the Receptor Density Algorithm. The algorithm is developed with inspiration from T cell signalling processes and has application in anomaly detection. Connections between the Receptor Density Algorithm and kernel density estimation with exponential smoothing are demonstrated. Finally, the paper evaluates the algorithm's performance on two types of anomaly detection problem.

Original language	English
Pages (from-to)	51-73
Number of pages	23
Journal	Theoretical Computer Science
Volume	481
DOIs	https://doi.org/10.1016/j.tcs.2012.10.057
Publication status	Published - 15 Apr 2013

Keywords

Anomaly detection
Artificial immune systems
Bio-inspired algorithms
Exponential smoothing
Kernel density estimation
Recurrent equations
T cell signalling
Weighted moving average

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title = "The receptor density algorithm",

abstract = "This paper describes the biological and theoretical foundations of a new Artificial Immune System the Receptor Density Algorithm. The algorithm is developed with inspiration from T cell signalling processes and has application in anomaly detection. Connections between the Receptor Density Algorithm and kernel density estimation with exponential smoothing are demonstrated. Finally, the paper evaluates the algorithm's performance on two types of anomaly detection problem.",

keywords = "Anomaly detection, Artificial immune systems, Bio-inspired algorithms, Exponential smoothing, Kernel density estimation, Recurrent equations, T cell signalling, Weighted moving average",

author = "Owens, {Nick D.L.} and Andrew Greensted and Jon Timmis and Andy Tyrrell",

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AU - Tyrrell, Andy

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AB - This paper describes the biological and theoretical foundations of a new Artificial Immune System the Receptor Density Algorithm. The algorithm is developed with inspiration from T cell signalling processes and has application in anomaly detection. Connections between the Receptor Density Algorithm and kernel density estimation with exponential smoothing are demonstrated. Finally, the paper evaluates the algorithm's performance on two types of anomaly detection problem.

KW - Anomaly detection

KW - Artificial immune systems

KW - Bio-inspired algorithms

KW - Exponential smoothing

KW - Kernel density estimation

KW - Recurrent equations

KW - T cell signalling

KW - Weighted moving average

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VL - 481

SP - 51

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JO - Theoretical Computer Science

JF - Theoretical Computer Science

ER -

The receptor density algorithm

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Keywords

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