Honours and Masters project

Classical propositional logic (CPL) captures our basic understanding of the linguistic connectives “and”, “or” and “not”. It also provides a very good basis for digital circuits. But it does not account for more sophisticated linguistic notions such as “always”, “possibly”, “believed” or “knows”. Philosophers therefore invented many different non-classical logics which extend CPL with further operators for these notions.

Cyber Threat Intelligence (CTI) plays a vital role in today's cybersecurity landscape by collecting and analysing data about current and potential threats, providing insights to better understand, mitigate and respond in this ever-evolving environment. A core component of CTI is the identification of adversarial Tactics, Techniques, and Procedures (TTPs), which describe how attackers operate at a strategic and operational level.

Organisations continuously face cyberattacks that generate large volumes of security alerts from intrusion detection systems, SIEM platforms, and other security monitoring tools. Many of these alerts may be false positives, low priority, or related to different stages of the same attack. Analysing individual alerts in isolation can therefore overwhelm security analysts and make it difficult to identify the threats that require immediate attention.

Online fraud, also referred to as cyberscams, is increasingly becoming a cybersecurity problem that technical cybersecurity specialists are unable to effectively detect. Given the difficulty in the automatic detection of scams, the onus is often pushed back to humans to detect. Gamification and awareness campaigns are regularly researched and implemented in workplaces to prevent people from being tricked by scams, which may lead to identity theft or conning individuals out of money.

This project is part of of our broader initiative in the Environmental Informatics Hub to develop technologies that  effectively support conservation and biodiversity research. It develops methods for the autonomous long-term monitoring of coral reef health.

We are part of the Tara Coral mission (see below) and work closely with marine biologists to develop automated methods that will meet real-world needs for this important area. 

Description:

Magnetic Resonance Imaging (MRI) stands as a cornerstone in medical imaging, providing non-invasive, high-resolution images of the human body's internal structures.  Brain tumor segmentation from MRI scans is essential for precise diagnosis and treatment planning. MRI provides detailed views of brain structures and abnormalities, but challenges like image noise, contrast imperfections and tumor variations can make segmentation difficult.

Description:

The early detection of neurological abnormalities through Magnetic Resonance Imaging (MRI) is crucial in the medical field, potentially leading to timely interventions and better patient outcomes. However, the traditional diagnostic process is often time-consuming and subject to human error. This project seeks to improve this aspect by employing deep learning for anomaly detection in MRI scans, exclusively using images from healthy participants for model training [1].

This project involves building a system that processes IoMT data(such as heart rate, blood pressure, or glucose levels) from wearable devices to monitor patient health in real time.

The system uses machine learning to detect anomalies and alert healthcare providers or caregivers. It includes data preprocessing, model training, and a simple dashboard for visualization.

This project focuses on implementing an AI-powered digital twin for intelligent electric vehicle (EV) traffic management in smart cities, utilising the YOLO algorithm. It develops a basic digital twin system designed to monitor and manage EV traffic in urban areas. The system detects and tracks EVs using feeds from traffic cameras. The digital twin simulates traffic flow, providing a visualisation of EV movement, congestion points, and route patterns.