An Agent-based Classification Model
The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2007
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| Online Access: | https://eprints.nottingham.ac.uk/587/ |
| _version_ | 1848790437649711104 |
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| author | Gu, Feng Aickelin, Uwe Greensmith, Julie |
| author_facet | Gu, Feng Aickelin, Uwe Greensmith, Julie |
| author_sort | Gu, Feng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal
and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer
systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the
feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes
it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment. |
| first_indexed | 2025-11-14T18:12:36Z |
| format | Conference or Workshop Item |
| id | nottingham-587 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:12:36Z |
| publishDate | 2007 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-5872020-05-04T20:28:52Z https://eprints.nottingham.ac.uk/587/ An Agent-based Classification Model Gu, Feng Aickelin, Uwe Greensmith, Julie The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment. 2007 Conference or Workshop Item PeerReviewed Gu, Feng, Aickelin, Uwe and Greensmith, Julie (2007) An Agent-based Classification Model. In: The 9th European Agent Systems Summer School (EASSS 2007), Durham, UK. |
| spellingShingle | Gu, Feng Aickelin, Uwe Greensmith, Julie An Agent-based Classification Model |
| title | An Agent-based Classification Model |
| title_full | An Agent-based Classification Model |
| title_fullStr | An Agent-based Classification Model |
| title_full_unstemmed | An Agent-based Classification Model |
| title_short | An Agent-based Classification Model |
| title_sort | agent-based classification model |
| url | https://eprints.nottingham.ac.uk/587/ |