Finance

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.

This study measured the value-at-risk (VaR) of five Nigerian banks using an innovative approach of incorporating sentiments from financial tweets.

This study applied a machine learning technique (support vector machine) for sentiment analysis of Nigerian banks’ Twitter data within 2 years, from 1st January 2017 to 31st December 2018.