Dendritic Cells for Anomaly Detection
Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen pre...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2006
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| Online Access: | https://eprints.nottingham.ac.uk/598/ |
| _version_ | 1848790440846819328 |
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| author | Greensmith, Julie Twycross, Jamie Aickelin, Uwe |
| author_facet | Greensmith, Julie Twycross, Jamie Aickelin, Uwe |
| author_sort | Greensmith, Julie |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop
an intrusion detection system based on a novel concept in
immunology, the Danger Theory. Dendritic Cells (DCs) are
antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining
signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic. |
| first_indexed | 2025-11-14T18:12:39Z |
| format | Conference or Workshop Item |
| id | nottingham-598 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:12:39Z |
| publishDate | 2006 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-5982020-05-04T20:29:54Z https://eprints.nottingham.ac.uk/598/ Dendritic Cells for Anomaly Detection Greensmith, Julie Twycross, Jamie Aickelin, Uwe Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic. 2006 Conference or Workshop Item PeerReviewed Greensmith, Julie, Twycross, Jamie and Aickelin, Uwe (2006) Dendritic Cells for Anomaly Detection. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver, Canada. |
| spellingShingle | Greensmith, Julie Twycross, Jamie Aickelin, Uwe Dendritic Cells for Anomaly Detection |
| title | Dendritic Cells for Anomaly Detection |
| title_full | Dendritic Cells for Anomaly Detection |
| title_fullStr | Dendritic Cells for Anomaly Detection |
| title_full_unstemmed | Dendritic Cells for Anomaly Detection |
| title_short | Dendritic Cells for Anomaly Detection |
| title_sort | dendritic cells for anomaly detection |
| url | https://eprints.nottingham.ac.uk/598/ |