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

^*Awdur cyfatebol y gwaith hwn

Adran Cyfrifiadureg

University of York

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

8 Dyfyniadau (Scopus)

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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.

Iaith wreiddiol	Saesneg
Tudalennau (o-i)	51-73
Nifer y tudalennau	23
Cyfnodolyn	Theoretical Computer Science
Cyfrol	481
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1016/j.tcs.2012.10.057
Statws	Cyhoeddwyd - 15 Ebr 2013

Mynediad at Ddogfen

10.1016/j.tcs.2012.10.057

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@article{887e3ed1b7d34ccf9c347fc1057a35bb,

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",

year = "2013",

month = apr,

day = "15",

doi = "10.1016/j.tcs.2012.10.057",

language = "English",

volume = "481",

pages = "51--73",

journal = "Theoretical Computer Science",

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T1 - The receptor density algorithm

AU - Owens, Nick D.L.

AU - Greensted, Andrew

AU - Timmis, Jon

AU - Tyrrell, Andy

PY - 2013/4/15

Y1 - 2013/4/15

N2 - 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.

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

UR - https://www.scopus.com/pages/publications/84875825864

U2 - 10.1016/j.tcs.2012.10.057

DO - 10.1016/j.tcs.2012.10.057

M3 - Article

AN - SCOPUS:84875825864

SN - 0304-3975

VL - 481

SP - 51

EP - 73

JO - Theoretical Computer Science

JF - Theoretical Computer Science

ER -

The receptor density algorithm

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