Machine Learning

I was invited by ISACA Ireland Chapter to deliver a talk. My talk was on how large language models can be leveraged to detect psychological vulnerabilities in phishing emails.

Invited by the Women in Machine Learning and Data Science (WiMLDS), Accra, Ghana.

This research project explores the use of generative artificial intelligence, large language models (LLMs) to enhance human-centred cybersecurity defences against sophisticated attacks such as phishing attacks. The goal is to augment human expertise and enable security teams to stay ahead of evolving attack techniques at machine speed and scale.

Invited by the Knowledge and Skills Forum aimed at building data science skills for those in developing countries.

This paper proposed a three-stage feature selection method that effectively reduces the dimensionality of the data and considers the varying importance of the different feature groups in the classification of ransomware families.

Invited by Dr Hilary Murray to deliver this talk at Dublin City University, Department of Computer Science to undergraduate students studying “Introduction to Machine Learning”

Invited by HentrosoftAI.

Invited by the Commonwealth Scholarship Commission in the UK. The talk targets commonwealth and developing countries professionals on how they can use these technologies for societal benefit.

This paper describes the different approaches followed in arriving at an optimal solution model that effectually predicts whether a person has been in close proximity to an infected individual using a gradient boosting algorithm and time series feature extraction.

This PhD research project focuses on developing an adaptive intrusion detection system that leverages advanced machine learning algorithms to detect and mitigate emerging ransomware threats. By designing novel ML techniques capable of analysing large-scale datasets of ransomware samples and benign files, the project aims to create a highly accurate and responsive defence against rapidly evolving ransomware attacks.