Cybersecurity stands as a paramount concern in today’s digital landscape, with the proliferation of cyber threats posing significant risks to individuals, organizations, and governments worldwide. In response to this escalating challenge, the fusion of machine learning (ML) techniques with cyber threat intelligence (CTI) data emerges as a promising approach which can help develop defense mechanisms against malicious activities. This thesis investigates the application of ML algorithms to CTI datasets for predicting the categories of cyber attacks, aiming to model CTI datasets for ML and develop a predictive model capable of discerning attack categories amidst the complex cyber threat landscape. Methodologically, the research employs a systematic approach to dataset processing and ML classifier training, encompassing feature identification, engineering, and selection, as well as iterative model refinement and validation. The envisioned outcomes include the ability to model CTI datasets to effectively predict attack categories with confidence. Through extensive data processing, exploratory data analysis, and feature engineering, this research contributes to the advancement of cybersecurity by providing a robust framework for modelling CTI data and accurately predicting cyber attack categories
