Dr Ian Mitchell


Dr Ian Mitchell
NameDr Ian Mitchell
Job titleDirector of Programmes - CCE
Research institute
Primary appointmentComputer Science
Email addressI.Mitchell@mdx.ac.uk
ORCIDhttps://orcid.org/0000-0002-3882-9127
Contact categoryResearcher

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. 119-138

Quality 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. 42-43

BMAR - 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. 365-397

dAppER: 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/ICGS3.2019.8688143

DaP∀ : 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. 275-281

SMERF: 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. 203-225

CABots 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.2018.00079

Bitcoin 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_2

Deconstruct 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_1

Programming 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. 36-45

PlaNeural: 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.2016.07.425

A 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.2015.03.001

Computer 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/s11571-014-9282-4

Compensatory 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.2013.06.003

A 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 2011

KCMAC-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.2012.06.028

Self 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 2010

Quality 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 2008

Selection 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.2006.011037

Dynamics 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. 226-229

Threading 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/s00500-005-0488-6

A 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.
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