Analyzing security of ECG biometrics (available)

Biometric systems rely on physiological or behavioural characteristics that can be measured by sensors to verify identity. Electrocardiogram (ECG)-based biometrics is one of the new and most promising types. ECG signals measure the electrical activity of the heart and several studies have found them suitable for human identification. To match the signals with the registered … full description “Analyzing security of ECG biometrics (available)”

Author Attribution of Binaries (available)

Attributing binaries, whether malicious or benign, is a difficult and time consuming task however, there is an increase demand for this either for attributing cyber attacks or preventing plagiarism. The goal of this project is to use machine learning to predict authorship of binaries. You will use a corpus of open source software either for … full description “Author Attribution of Binaries (available)”

Building a Full Causality Chain Across an Enterprise System (completed)

Data Provenance refers to records of the inputs, entities, systems and process that influence data of interest, providing a historical record of the data and its origins. To provide a holistic view of the data provenance in an enterprise system, the provenance records of the activities carried out on a client workstation is important. Last … full description “Building a Full Causality Chain Across an Enterprise System (completed)”

Computer Vision for Extreme Environments (available)

The use of data from extreme environments in computer vision have shown an increase of interest in recent years as drones and autonomous vehicles were introduced into new uses. Nuclear plants, deep underwater and space vehicles are some of the areas computer vision can be applied to develop a fully autonomous system. Furthermore, the development … full description “Computer Vision for Extreme Environments (available)”

Interactive Visualisation of Disentangled Representations (available)

This project aims to develop an interactive visualisation toolkit based on existing technologies (IPython & Plotly) that will assist researchers in debugging and understanding complex models in the area of representation learning. Representation learning is a sub-field of machine learning that focuses on developing techniques for representing objects that exist in high-dimensional space (e.g. faces … full description “Interactive Visualisation of Disentangled Representations (available)”

Machine Learning for Crystal Structure Prediction (available)

Crystal Structure Prediction (CSP) is one of the major problems in computational chemistry with numerous applications in real life. This is essentially the (global) minimisation of a continuous, high-dimensional, complicated function. Many heuristic methods have been proposed for CSP and recently new methods based on Machine Learning were introduced. The goal of this project is … full description “Machine Learning for Crystal Structure Prediction (available)”

Machine Learning for Cyber-Physical Systems Monitoring (available)

Hybrid automata (HA) are a formal model for cyber-physical systems, i.e., systems characterised by digital components (discrete) that control and interact with the physical environment (continuous). HAs have been applied to system designs in numerous domains including avionics, automotive, medical devices, and robotics. Formal verification of HAs can establish, with provable correctness guarantees, whether or … full description “Machine Learning for Cyber-Physical Systems Monitoring (available)”

Machine Learning Library for OCaml (available)

Frameworks for machine learning include Python’s TensorFlow [1]. These frameworks provide standard ways of specifying models that can be optimised by machine learning algorithms. OCaml [2] is a mature functional programming language with an expressive type system. DecML [3] is a prototype OCaml extension that facilitates implementing machine learning tasks, based on specifying models as … full description “Machine Learning Library for OCaml (available)”

Machine Learning vs Machine Learning in Malware Evasion (available)

Machine learning is a popular approach to signature-less malware detection because it can generalize to new (unseen) malware families. Some recent works have proposed the use of AI/ML-powered malware to bypass machine learning anti-malware systems. The goal of the project is to model the system of malware vs anti-malware systems as two opponents using various … full description “Machine Learning vs Machine Learning in Malware Evasion (available)”

Mitigating Anti-Sandboxing Tricks used by Malware (available)

Aims: Detecting and Mitigating some Evasion Techniques used by Malware Background: Several malware samples exploit advanced tactics to detect whether they are run in a sandboxed/virtual analysis environment. In such cases, malware samples do not perform any malicious actions to evade analysis and detection by security researchers. The goal of the project is to analyse … full description “Mitigating Anti-Sandboxing Tricks used by Malware (available)”

Mixed Nash Equilibria in Net Coordination Games (available)

Net Coordination Games form a special class of many-player games with several applications in Theoretical Computer Sciene, Multi Agent Systems, and Semi Supervised Learning.  Nash equilibria correspond to the stable outcomes and they are the prominent solution concept in games. It is known that Net Coordination Games possess a pure Nash equilibrium, but unfortunately, it … full description “Mixed Nash Equilibria in Net Coordination Games (available)”

OSN Mining Platform – Building a public dataset for fake news research (available)

Project Description According to the Statista, in 2018 the UK had 44 million (66% of the population) active Online Social Networks (OSN) users. Whereas, globally, active OSN user population is 3,397 million. The most popular reason for using the OSN is to stay in touch with friends and family (42% of respondents) and stay up … full description “OSN Mining Platform – Building a public dataset for fake news research (available)”

Predicting Debug Symbols for Closed Source Binaries (completed)

Reverse engineering binaries, whether malicious or benign, is made more difficult by the absence of debug information. Variables and functions have had their identifiers “stripped”, so reverse engineers have to manually name them during analysis based on human understanding of the code functionality. The goal of this project is to use machine learning to predict … full description “Predicting Debug Symbols for Closed Source Binaries (completed)”

Transparent Machine Learning – Shining the light in a black box world (available)

Project Description Autonomy, based on Artificial Intelligence (AI), is at the very centre of many existing and future innovative and enabling technologies including autonomous vehicles, urban air mobility, smart cities and Industry 4.0. All decisions made by an AI system are based on the underlying algorithm design and its training/profiling set. An AI algorithm whether … full description “Transparent Machine Learning – Shining the light in a black box world (available)”