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Dr Matthew Banton


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Lecturer in Computer Science

  • I am an Early Career researcher interested in cybersecurity, network anomaly detection AI, and Machine Learning. View my Orchid ID.


Teaching and supervision

Research interests for potential research students

  • Cybersecurity
  • Network Anomaly Detection
  • Machine Learning
  • AI

Research

My PhD is in AI and Cybersecurity entitled A Deep Learning-based Approach to Identifying and Mitigating Network Attacks Within SDN Environments Using Non-standard Data Sources Network Anomaly Detection.

I am interested in:

  • Network Anomaly Detection
  • Machine Learning
  • Artificial Intelligence
  • Cybersecurity
  • A Trust-Based Cooperative System for Efficient Wi-Fi Radio Access Networks  
  • IEEE Access  
  • 2023 | Journal article  
  • DOI: 10.1109/ACCESS.2023.3338177  
  • Contributors: Alessandro Raschell√†; Max Hashem Eiza; Michael Mackay; Qi Shi; Matthew Banton  

  • Model-Based Security Assessment on the Design of a Patient-Centric Data Sharing Platform  
  • 2022 | Book chapter  
  • DOI: 10.1007/978-3-031-16011-0_5  
  • Contributors: Matthew Banton; Thais Webber; Agastya Silvina; Juliana Bowles  
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  • Conflict-Free Access Rules for Sharing Smart Patient Health Records  
  • 2021 | Book chapter  
  • DOI: 10.1007/978-3-030-91167-6_3  
  • Contributors: Matthew Banton; Juliana Bowles; Agastya Silvina; Thais Webber  
  •  
  • Design of a Trustworthy and Resilient Data Sharing Platform for Healthcare Provision  
  • 2021 | Book chapter  
  • DOI: 10.1007/978-3-030-86507-8_14  
  • Contributors: Matthew Banton; Juliana Bowles; Agastya Silvina; Thais Webber  
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  • Intrusion Detection Using Extremely Limited Data Based on SDN  
  • Proceedings of 2020 IEEE 10th International Conference on Intelligent Systems Intrusion  
  • 2020-08-28 | Conference paper  
  • DOI: 10.1109/IS48319.2020.9199950  
  • Contributors Matthew Banton; Nathan Shone; William Hurst; Qi Shi  
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  • 6th International Conference on Information and Communication Technologies for Disaster Management, ICT-DM 2019
  • 2019-12-18 | Conference paper  
  • DOI: 10.1109/ICT-DM47966.2019.9032959  
  • Contributors Matthew Banton; Nathan Shone; William Hurst; Qi Shi:  
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  • Visualising Network Anomalies in an Unsupervised Manner Using Deep Network Autoencoders  
  • The Fourth International Conference on Applications and Systems of Visual Paradigms VISUAL 2019
  • 2019-06-30 | Conference paper  
  • Contributors Matthew Banton; Nathan Shone; William Hurst; Qi Shi

Last updated 08 April 2024