Chris McDermott
First Name: Christopher
Surname: McDermott
Position: Lecturer
Telephone: +44 (0) 1224 262709

Chris is a dynamic and professional Lecturer with over 15 years sector leading academic and teaching experience in the field of Computer Networking & Security. Proven to provide high quality, innovative teaching using dynamic methods and models of delivery, through which learners are motivated, engaged and challenged to realise their full potential. Remains quick to familiarise himself with the latest technologies and industry developments, demonstrating a logical and analytical approach to solving complex problems and issues. Able to communicate confidently and successfully with learners, colleagues and industry professionals, developing strong and sustainable relationships at all levels. Enjoys working collaboratively on sector leading projects and research areas with a particular interest in network security. 

Duties and Responsibilities

Principal Duties include:

  • BSc Computer Network Management & Design Course Leader
  • BSc Cybersecurity Course Leader
  • Lecturer in Secure Systems
  • Researcher in Security and Privacy

 Principal teaching is in Network/CyberSecurity on:

  • BSc Computer Science
  • BSc Computer Network Management & Design
  • BSc Cybersecurity
  • MSc CyberSecurity

 Current teaching on Modules:

  • CM3123 Programming for System Administrators
  • CM3103 Computing Network Management
  • CMM615 CCNA Security
  • Research Active in the Security and Privacy research group.
  • CCNA and CCNA Security Qualified.
  • Member of General Teaching Council for Scotland (GTCs)
  • Fellow of the Higher Education Academy (Currently pursuing)

Research Interests

Member of the Security and Privacy and Machine Learning research groups.

Principal research interest is in identifying security vulnerabilities and solutions to the current issues relating to network and internet security. 

To improve the detection and analysis of threats we focus on developing new machine learning algorithms and techniques and apply these across a varied landscape from Oil and Gas to Healthcare, Transport and Agriculture. 

Modern interconnected systems generate vast amounts of data, as part of our work we develop interactive visualisation models to aid in comprehension and effective decision making, based on this data.

Current projects include solutions aimed at the Internet of Things (IoT): We are currently developing:

  • A visualisation model to help improve detection and analysis of threats facing the Internet of Things (IoT).
  • A solution to improve data transfer from SMART tractors
  • An integrated IoT environment to enhance remote medical care and in-patient monitoring.


  • M. Nicho, C. D McDermott, “Dimensions of ‘Socio’ Vulnerabilities of Advanced Persistent Threats" In 2019 27th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2019 (submitted)
  • McDermott, Christopher D, John P. Isaacs., 2019. Towards a Conversational Agent for Threat Detection in the Internet of Things. In 2019 International Conference On Cyber Situational Awareness, Data Analytics And Assessment, Cyber SA 2019.  
  • McDermott, Christopher D., John P. Isaacs, and Andrei V. Petrovski., 2018. Evaluating User Awareness and Perception of Security and Privacy within the Internet of Things (IoT). Informatics (Human Factors in Security and Privacy in IoT (HFSP-IoT)), Vol. 5.
  • McDermott, C. D., Haynes, W. & Petrovski, A. V., 2018. Threat Detection and Analysis in the Internet of Things using Deep Packet Inspection. International Journal on Cyber Situational Awareness, Vol. 3, No. 1, pp. 61-83.
  • McDermott, C.D. et al., 2018. Towards Situational Awareness of Botnet Activity in the Internet of Things. In 2018 International Conference On Cyber Situational Awareness, Data Analytics And Assessment, Cyber SA 2018.
  • McDermott, C.D. et al., 2018. Botnet Detection in the internet of Things using Deep Learning Approaches. In 2018 International Joint Conference on Neural Networks.
  •  McDermott, C.D., & Petrovski, A., 2017. Investigation of Computational Intelligence Techniques for Intrusion Detection in Wireless Sensor Networks. International Journal of Computer Networks & Communications (IJCNC), 9(4), pp.45-56.