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The human immune system is a robust, decentralised, error tolerant and complex natural system which protects the body from invading organisms and regulates bodily functions. These properties are desirable within a computing context as computer systems become increasingly complex in nature, equally complex solutions are sought. If the human immune system can effectively detect a multitude of viruses, then surely a computational immune system can detect computer viruses?
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In our research, the immune system is used as inspiration for the development of novel algorithms and computer systems which have the potential to address complex real world problems, termed artificial immune systems. The model development is achieved through interdisciplinary collaboration with practical immunologists, ensuring that the latest developments in immunology are incorporated into our systems. In particular, we research the development of techniques based on the innate immune system and mechanisms derived from immune network models.
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One algorithm developed through our approach is the Dendritic Cell Algorithm, based on the behaviour of the dendritic cells of the innate immune system. This algorithm performs a combination of classification and correlation through the incorporation of context data. Applications of this algorithm include the detection of intrusions in computer networks, physical robotic security and thrill/fear discrimination for the entertainment industry. The immune network research is applied to evolutionary robotics using a virtual network of antibodies. We also investigate the use of immune memory when applied to time-series data for the detection of repeating patterns termed motifs with a high degree of accuracy. Modelling and simulation of the immune system is also performed within IMA, forming a computational immunology strand. Immune phenomena currently modelled includes the aging process of the human immune system in addition to other complex immune based mechanisms.
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Current Projects
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IMA Lead
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Value
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Funder
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Reference
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| Horizon: Digital Economy Hub at University of Nottingham |
Aickelin (CI) |
£12,459,687 |
EPSRC |
EP/G065802/1 |
| Centre for Plant Integrative Biology |
Garibaldi (CI) |
£9,800,000 |
BBSRC |
BBD0196131 |
| Next Generation Decision Support: Automating the Heuristic Design Process |
Garibaldi (CI) |
£2,666,765 |
EPSRC |
EP/D061571/1 |
| Novel Approaches to Radio Therapy Planning and Scheduling |
Garibaldi (CI) |
£268,315 |
EPSRC |
EP/C549511/1 |
| Hyper-heuristics for Scheduling, Rostering and Routing |
Garibaldi (PI) |
£31,828 |
EPSRC |
EP/D027039/1 |
| Total Current Principal Investigator |
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£31,828 |
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| Total Current Co-Investigator |
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£25,194,767
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| Total Current Funding |
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£25,226,595 |
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Previous Projects
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| Research Training Network for Bioinformatics |
Garibaldi (PI) |
£1,240,000 |
EU |
2004-007597 |
| Danger Theory: The Missing Link Between AIS and IDS |
Aickelin (PI) |
£657,407 |
EPSRC |
GR/S47809/01 |
| Towards More General Optimisation/Search Systems |
Garibaldi (CI) |
£422,908 |
EPSRC |
GR/S70197/01 |
| Computational Intelligence for Biopattern Analysis |
Garibaldi (PI) |
£320,000 |
EU |
2002-23111 |
| Service Level Agreement Based Schedule Heuristics |
Garibaldi (PI) |
£141,492 |
EPSRC |
GR/S67661/01 |
| A Novel Routing Protocol for Large Scale Disconnected Environments |
Radenkovic (PI) |
£86,242 |
EPSRC |
EP/D062659/1 |
| Multi-Sensor Data Fusion For Threat Analysis |
Aickelin (PI) |
£79,285 |
EPSRC |
CASE/SmithInst |
| The Supervisor: Towards A Human Scheduling Algorithm |
Aickelin (PI) |
£69,282 |
EPSRC |
GR/R92899/01 |
| Automated Protocol for Protein Structure Comparison |
Garibaldi (CI) |
£66,314 |
BBSRC |
BB/C511764/1 |
| Dendritic Cell Algorithm for GPS Multipath Mitigation |
Greensmith (CI) |
£13,000 |
EPSRC |
EP/E018580/1 |
| Total Previous Principal Investigator |
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£2,593,708
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| Total Previous Co-Investigator |
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£502,222
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Total Previous Funding
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£3,095,930
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