AI-Enhanced Cybersecurity for Smart Health Systems

The increasing digitization in healthcare through IoT devices, electronic health records (EHRs), and telemedicine platforms has improved patient care but also exposed sensitive data and critical systems to cyber threats. To address these vulnerabilities, this PhD project, titled "AI-Enhanced Anomaly Detection for Cybersecurity in Smart Health Systems," aims to develop a comprehensive, AI-driven framework specifically for healthcare cybersecurity.

The primary objective is to create a system capable of real-time detection of potential threats by analysing network traffic patterns, device usage, and user behaviour to identify anomalies indicative of phishing attacks, malware intrusions, and insider threats. A distinguishing aspect of this project is the incorporation of privacy-preserving techniques, such as differential privacy and federated learning, ensuring that the AI processes data securely without exposing sensitive information.

Deliverables for this research include a scalable AI model that can be implemented across healthcare networks to provide continuous monitoring, an adaptive phishing detection module, and a report detailing protocol recommendations to enhance healthcare-specific cybersecurity standards. A critical component of this project is the development of a phishing detection model, which will employ natural language processing (NLP) techniques to identify phishing attempts targeted at healthcare professionals. By training the model on domain-specific datasets, it will recognize common phishing tactics and healthcare-related terminology, improving its effectiveness in detecting tailored phishing attacks.

Another major deliverable is an anomaly detection model, optimized for healthcare environments, capable of identifying unusual network activity and suspicious device behaviours. This model will be designed to alert security teams of potential malware infections or unusual insider actions, such as unauthorized data access, that could indicate a security breach. To validate these systems, the research will include extensive simulation testing within a controlled environment, assessing the framework's efficacy under realistic threat scenarios and quantifying the framework’s detection accuracy, false-positive rate, and response speed. In addition to these technical deliverables, the project will also produce a set of best practice guidelines for integrating AI-driven cybersecurity systems in healthcare, covering system scalability, privacy compliance, and cost-effective deployment strategies for small and medium-sized healthcare facilities with limited cybersecurity resources.

By achieving these deliverables, this project will not only strengthen real-time defences for healthcare systems but also establish a robust, adaptable cybersecurity model that addresses the unique demands of the healthcare sector.

How to apply

Formal applications can be submitted via the University of Bradford web site; applicants will need to register an account and select 'Full-time PhD in Computer Science' as the course, and then specify the project title in the 'Research Proposal' section.

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