PhD Studentship: Developing Sustainable Intrusion Detection Systems for Industrial IoT by Leveraging Digital Twin and Signal Processing

The rapid adoption of Industrial Internet of Things (IIoT) technologies has transformed manufacturing, offering greater efficiency, real-time monitoring, and data-driven decision-making. However, this interconnectivity introduces significant cybersecurity vulnerabilities, leaving systems exposed to cyber-attacks. Traditional intrusion detection systems (IDS) often fall short in handling the real-time, large-scale data demands of IIoT and lack sustainability considerations, such as energy efficiency.

This research proposes a sustainable, high-performance IDS that leverages digital twin technology and advanced signal processing to detect cyber threats in real-time while minimizing energy consumption. Digital twins, as virtual replicas of physical systems, enable continuous monitoring and anomaly detection with minimal latency. Combined with efficient signal processing, this approach enhances detection accuracy while optimizing resource use, supporting cybersecurity and sustainability in IIoT networks.

This study aims to develop a low-energy IDS solution tailored to IIoT’s unique security needs, balancing robust threat detection with reduced energy demands. By integrating digital twins and resource efficient signal processing, this research sets a new standard for sustainable cybersecurity in IIoT. The PhD candidate undertaking this research will gain expertise in cybersecurity, IIoT, signal processing, and artificial intelligence. The project offers access to state-of-the-art research facilities at the University of Greenwich, where cutting-edge cybersecurity solutions for industrial systems are developed. Throughout the project, the candidate will be encouraged to publish findings in high-impact journals, present at international conferences, and contribute to the growing body of knowledge on IIoT security.

This PhD project presents an exciting opportunity for researchers passionate about cybersecurity, artificial intelligence, and industrial automation to contribute to a high-impact area of research. The growing interconnectivity of industrial systems highlights the urgency of developing robust and efficient security mechanisms, making this research both relevant and essential for future IIoT deployments. The successful development of a Digital Twin-enabled IDS will not only improve the cybersecurity of industrial networks but also establish a foundation for further advancements in intelligent, self-learning security systems. The PhD candidate will work under the primary supervision of Dr Kamran Pedram at the Centre for Sustainable Cyber Security (CS2) https://www.gre.ac.uk/research/groups/sustainable-cyber-security-cs2

The University of Greenwich (through CS2) has been recently recognised by the UK government as a NCSC Academic Centre of Excellence in Cyber Security Research (https://www.ncsc.gov.uk/information/academic-centres-excellence-cyber-security-research)

Please read this information before making an application. Information on the application process is available via the apply button above. Applications need to be made online via this link. No other form of application will be considered.

All applications must include the following information. Applications not containing these documents will not be considered.

• Scholarship Reference Number (CS2-FES-01-24)– included in the personal statement section together with your personal statement as to why you are applying

• a CV including 2 referees *

• academic qualification certificates/transcripts and IELTs/English Language certificate if you are an international applicant or if English is not your first language or you are from a country where English is not the majority spoken language as defined by the UK Border Agency *

*upload to the qualification section of the application form. Attachments must be a PDF format.

Before submitting your application, you are encouraged to liaise with the Lead Supervisor on the details above

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