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)”

Machine Learning for Cyber-Physical Systems Verification (available)

Hybrid automata (HA) [1] 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 is crucial for such safety-critical applications, as … full description “Machine Learning for Cyber-Physical Systems Verification (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)”

Mathematical Modelling of Cyber-Attackers vs Defenders using AI/ML (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 (for instance, adversarial machine learning). The goal of the project is to model the system of malware vs anti-malware systems … full description “Mathematical Modelling of Cyber-Attackers vs Defenders using AI/ML (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)”