CAGE: Consensus Algorithm Genetically Enhanced

Conference paper


Mitchell, I. and Kamil, M. 2023. CAGE: Consensus Algorithm Genetically Enhanced. Virtual 15th International Conference on Global Security, Safety & Sustainability. Online Springer.
TypeConference paper
TitleCAGE: Consensus Algorithm Genetically Enhanced
AuthorsMitchell, I. and Kamil, M.
Abstract

Blockchain is a disruptive technology and relies on the development of consensus algorithms, CA. Currently, there are many CAs proposed and this paper explores a new avenue of nature-inspired CAs. In particular, emphasis is on selection techniques used in canonical genetic algorithms, GAs, and fatiguing.
The premise is that selection techniques used in GAs could be adapted and used to select nodes responsible for adding blocks in permissioned blockchain networks. This was then tested on a blockchain network and executed many times and compared to another CA for permission blockchain networks, Proof-of-Elapsed-Time, PoET.
The results are interesting and show an improvement in transaction throughput and block creation. Our initial results, however, had a biased node selection and could be vulnerable to take-over attacks. Therefore, a fatiguing element was introduced and the experiments were repeated with the results showing a less biased node selection.
The overall contribution is the exploration of nature as a solution to CAs. The initial results are comparable and places better than existing CAs.

KeywordsBlockchain; Consensus Algorithms; Evolutionary Computation
Sustainable Development Goals12 Responsible consumption and production
Middlesex University ThemeSustainability
ConferenceVirtual 15th International Conference on Global Security, Safety & Sustainability
ISSN1613-5113
Electronic2363-9466
PublisherSpringer
Publication process dates
Deposited18 Apr 2023
Submitted06 Apr 2023
Accepted06 May 2023
Output statusPublished
First submitted version
File Access Level
Controlled
LanguageEnglish
Permalink -

https://repository.mdx.ac.uk/item/8q584

  • 66
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

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
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
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
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.
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
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
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.
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.
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
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
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
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
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
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.
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
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
Selection enthusiasm.
Mitchell, I. and Agrawal, A. 2006. Selection enthusiasm. in: 6th International Conference on Simulated Evolutionand Learning, Hefei, China. Proceedings. Heidelberg Springer Verlag.
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