Dr Ian Mitchell
| Name | Dr Ian Mitchell |
|---|---|
| Job title | Director of Programmes - CCE |
| Research institute | |
| Primary appointment | Computer Science |
| Email address | I.Mitchell@mdx.ac.uk |
| ORCID | https://orcid.org/0000-0002-3882-9127 |
| Contact category | Researcher |
Biography
Biography Programme Leader of various programmes since 2003. Recently, completed programme validation of BSc Cyber Security & Digital Forensics and successfully leads this programme and, over the years, have built up an experience of programme validations. Research interests include Blockchain Technology and it applications in both permissioned and permissionless networks.
Teaching Individual Project Blockchain Development
Employment
Programme Leader: BSc Computer Forensics
Middlesex University
01 Oct 2007
01 Jun 2016
Education and qualifications
Grants
Prizes and Awards
External activities
Research outputs
CAGE: Consensus Algorithm Genetically Enhanced
Mitchell, I. and Kamil, M. 2023. CAGE: Consensus Algorithm Genetically Enhanced. Virtual 15th International Conference on Global Security, Safety & Sustainability. Online Springer.Privacy, security and forensics in the Internet of Things (IoT)
Montasari, R., Carroll, F., Mitchell, I., Hara, S. and Bolton-King, R. (ed.) 2022. Privacy, security and forensics in the Internet of Things (IoT). Springer.IoT and cloud forensic investigation guidelines
Mitchell, I., Hara, S., Ibarra-Jiminez, J., Jahankhani, H. and Montasari, R. 2020. IoT and cloud forensic investigation guidelines. in: Jahankhani, H., Akhgar, B., Cochrane, P. and Dastbaz, M. (ed.) Policing in the Era of AI and Smart Societies Cham, Switzerland Springer. pp. 119-138Quality audits with Blockchain for healthcare in the UK
Mitchell, I. and Hara, S. 2019. Quality audits with Blockchain for healthcare in the UK. George, C., Whitehouse, D. and Duquenoy, P. (ed.) Health IT Workshop 2019. Middlesex University, London 07 - 08 Nov 2019 pp. 42-43BMAR - blockchain for medication administration records
Mitchell, I. and Hara, S. 2019. BMAR - blockchain for medication administration records. in: Jahankhani, H., Kendzierskyj, S., Jamal, A., Epiphaniou, G. and Al-Khateeb, H. (ed.) Blockchain and Clinical Trial: Securing Patient Data Cham, Switzerland Springer.Blockchain of custody, BoC
Mitchell, I., Hara, S., Jahankhani, H. and Neilson, D. 2020. Blockchain of custody, BoC. in: Jahankhani, H. (ed.) Cyber Security Practitioner's Guide World Scientific. pp. 365-397dAppER: decentralised application for examination reviews
Mitchell, I., Hara, S. and Sheriff, M. 2019. dAppER: decentralised application for examination reviews. 12th International Conference on Global Security, Safety & Sustainability. Northumbria University London, England 16 - 18 Jan 2019 IEEE. https://doi.org/10.1109/ICGS3.2019.8688143DaP∀ : Deconstruct and Preserve for all: a procedure for the preservation of digital evidence on solid state drives and traditional storage media
Mitchell, I., Ferriera, J., Anandaraja, T. and Hara, S. 2018. DaP∀ : Deconstruct and Preserve for all: a procedure for the preservation of digital evidence on solid state drives and traditional storage media. in: Jahankhani, H. (ed.) Cyber Criminology Cham, Switzerland Springer. pp. 275-281SMERF: Social Media, Ethics and Risk Framework
Mitchell, I., Cockerton, T., Hara, S. and Evans, C. 2018. SMERF: Social Media, Ethics and Risk Framework. in: Jahankhani, H. (ed.) Cyber Criminology Cham, Switzerland Springer. pp. 203-225CABots and other neural agents
Huyck, C. and Mitchell, I. 2018. CABots and other neural agents. Frontiers in Neurorobotics. 12, pp. 1-12. https://doi.org/10.3389/fnbot.2018.00079Bitcoin forensics: a tutorial
Neilson, D., Hara, S. and Mitchell, I. 2017. Bitcoin forensics: a tutorial. Jahankhani, H., Carlile, A., Emm, D., Hosseinian-Far, A., Brown, G., Sexton, G. and Jamal, A. (ed.) 11th International Conference on Global Security, Safety and Sustainability. London, UK 18 - 20 Jan 2017 Cham Springer. pp. 12-26 https://doi.org/10.1007/978-3-319-51064-4_2Deconstruct and preserve (DaP): a method for the preservation of digital evidence on solid state drives (SSD)
Mitchell, I., Anandaraja, T., Hara, S., Hadzhinenov, G. and Neilson, D. 2017. Deconstruct and preserve (DaP): a method for the preservation of digital evidence on solid state drives (SSD). Jahankhani, H., Carlile, A., Emm, D., Hosseinian-Far, A., Brown, G., Sexton, G. and Jamal, A. (ed.) 11th International Conference on Global Security, Safety and Sustainability. London, UK 18 - 20 Jan 2017 Cham Springer. https://doi.org/10.1007/978-3-319-51064-4_1Programming with simulated neurons: a first design pattern
Evans, C., Mitchell, I. and Huyck, C. 2016. Programming with simulated neurons: a first design pattern. PPIG 2016 - 27th Annual Workshop of the Psychology of Programming Interest Group. University of Cambridge, Cambridge, UK 07 - 10 Sep 2016 Psychology of Programming Interest Group. pp. 36-45PlaNeural: spiking neural networks that plan
Mitchell, I., Huyck, C. and Evans, C. 2016. PlaNeural: spiking neural networks that plan. 7th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2016. New York City, NY, USA 16 Jul 2016 Elsevier. pp. 198-204 https://doi.org/10.1016/j.procs.2016.07.425A comparison of simple agents implemented in simulated neurons
Huyck, C., Evans, C. and Mitchell, I. 2015. A comparison of simple agents implemented in simulated neurons. Biologically Inspired Cognitive Architectures. 12, pp. 9-19. https://doi.org/10.1016/j.bica.2015.03.001Computer forensics: challenges to evidential integrity
Mitchell, I. and Hara, S. 2014. Computer forensics: challenges to evidential integrity. in: Jennions, I. (ed.) Integrated vehicle health management: implementation and lessons learned Warrendale, PA, USA SAE International.Post and pre-compensatory Hebbian Learning for categorisation
Huyck, C. and Mitchell, I. 2014. Post and pre-compensatory Hebbian Learning for categorisation. Cognitive Neurodynamics. 8 (4), pp. 299-311. https://doi.org/10.1007/s11571-014-9282-4Compensatory Hebbian learning for categorisation in simulated biological neural nets
Huyck, C. and Mitchell, I. 2013. Compensatory Hebbian learning for categorisation in simulated biological neural nets. Biologically Inspired Cognitive Architectures. 6 (5), pp. 3-7. https://doi.org/10.1016/j.bica.2013.06.003A framework for digital investigations: a case study using BPB modifications
Mitchell, I. 2011. A framework for digital investigations: a case study using BPB modifications. 6th International Annual Workshop on Digital Forensics and Incident Analysis (WDFIA 2011). Kingston University, London Jul 2011KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning
Tian, K., Guo, B., Liu, G., Mitchell, I., Cheng, D. and Zhao, W. 2013. KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning. Neurocomputing. 101, pp. 24-31. https://doi.org/10.1016/j.neucom.2012.06.028Self organising maps with a point neuron model
Huyck, C. and Mitchell, I. 2013. Self organising maps with a point neuron model. Intl Conf. on Cognitive and Neural Systems.Genetic sequences: tracing the mutations of a disease.
Mitchell, I., Passmore, P. and Xu, K. 2010. Genetic sequences: tracing the mutations of a disease. IEEE VAST Symposium 2010 Challenge. Salt Lake City, Utah, USA 24 - 29 Oct 2010Quality assurance of curricula through the use of an integrated framework for programme validation.
Mitchell, I., Sheriff, M. and Georgiadou, E. 2008. Quality assurance of curricula through the use of an integrated framework for programme validation. Tempus JEP-27178-2006, Dissemination Workshop.. Yerevan, Armenia Sep 2008Selection enthusiasm.
Mitchell, I. and Agrawal, A. 2006. Selection enthusiasm. in: 6th International Conference on Simulated Evolutionand Learning, Hefei, China. Proceedings. Heidelberg Springer Verlag.MESSM: a framework for protein fold recognition using neural networks and support vector machines.
Mitchell, I., Jiang, N. and Wu, W. 2006. MESSM: a framework for protein fold recognition using neural networks and support vector machines. International Journal of Bioinformatics Research and Applications. 2 (4), pp. 381-393. https://doi.org/10.1504/IJBRA.2006.011037Dynamics in proportionate selection.
Mitchell, I., Agrawal, A., Litovski, I. and Passmore, P. 2005. Dynamics in proportionate selection. in: International Conference on Adaptive and Natural Computnig Alogorithms, Coimbra, Portugal. Proceedings. Vienna. Springer. pp. 226-229Threading with environment-specific score by artificial neural networks
Mitchell, I., Jiang, N. and Wu, W. 2006. Threading with environment-specific score by artificial neural networks. Soft Computing. 10 (4), pp. 305-314. https://doi.org/10.1007/s00500-005-0488-6A connectionist inference model for pattern-directed knowledge representation
Mitchell, I. and Bavan, A. 2000. A connectionist inference model for pattern-directed knowledge representation. Expert systems. 17 (2), pp. 106-113.1301
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