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Collaborative Federated Learning for Ransomware Attack Detection for Internet of Health Things ( IoHTs)

   Faculty of Engineering and Technology

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  Dr Shagufta Henna  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

This is a PhD project.

Supervisors: Dr. Shagufta Henna, Dr. Alan Davy

Project Summary

Ransomware attacks have become more widespread, and several attacks have made headline news over the last couple of years, healthcare, financial, and governmental organisations. These attacks have caused substantial financial losses and have significantly disrupted the operation of healthcare services. Furthermore, since the Internet of health Things (IoHTs) has emerged, the number of devices connected to the Internet has increased significantly exposing the healthcare to ransomware attacks. To mitigate ransomware and other cybersecurity attacks, cybersecurity analysts rely on Intrusion Detection Systems (IDSs) to detect malicious activities using various pattern matching mechanisms, such as signature-based or observing anomalous activities, i.e., anomaly-based. Existing cybersecurity approaches for malware/ransomware detection and mitigation for the Internet of Things (IoT) are ineffective owing to the heterogeneity and distributed nature of IoHTs.

This project aims to tackle ransomware attack detection issues in large-scale IoHTs. The proposed research aims at developing a practically deployable privacy-preserving cyber security solution to different families of ransomware, such as WannaCry, Petya, BadRabbit, and PowerGhost in IoHTs. The objective of developed methods is to analyse the various ransomware families developed using cutting-edge Machine / Deep Learning algorithms coupled with advanced encryption methods.

The project offers the candidate new opportunities to gain invaluable experience in cybersecurity, machine learning, and IoTs. The successful candidate will have the opportunity to work within a dynamic and multi-disciplinary team.

Candidate Qualifications/Requirements:

  • Master's degree in Computer Science or related disciplines such as Information Security, Cyber Security, Computer Networks, Artificial Intelligence/Big Data Analytics.
  • At least a 2:1 Honour’s degree/ Bachelor’s degree, or equivalent, in Computer Science or related disciplines.
  • Strong interest in Cyber Security, Artificial Intelligence, Machine Learning and IoTs.
  • Experience in Machine Learning, i.e., supervised, and unsupervised and tools (TensorFlow, PyTorch, Keras), Optimisation Theory, and Computer Networks.
  • Good analytical skills - knowledge of foundations of computer science, ability to think independently
  • Strong oral and written communication skills, in both plain English and academic language.
  • Ideally, publications in an international conference or journal as a primary author.

Application Process

  1. To apply for this PhD project, please complete the following application form and return to the [Email Address Removed] with all the relevant documentation by 5:00pm, Thursday 9th June.
  2. Please include "PHD.3" in the subject of your email.
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