  Cybersecurity faces challenges in identifying and mitigating ransomware,
which is important for protecting critical infrastructures. The absence of
datasets for distinguishing normal versus abnormal network behaviour hinders
the development of proactive detection strategies against ransomware. An
obstacle in proactive prevention methods is the absence of comprehensive
datasets for contrasting normal versus abnormal network behaviours. The dataset
enabling such contrasts would significantly expedite threat anomaly mitigation.
In this study, we introduce UGRansome2024, an optimised dataset for ransomware
detection in network traffic. This dataset is derived from the UGRansome data
using an intuitionistic feature engineering approach that considers only
relevant patterns in network behaviour analysis. The study presents an analysis
of ransomware detection using the UGRansome2024 dataset and the Random Forest
algorithm. Through encoding and feature relevance determination, the Random
Forest achieved a classification accuracy of 96% and effectively identified
unusual ransomware transactions. Findings indicate that certain ransomware
variants, such as those utilising Encrypt Decrypt Algorithms (EDA) and Globe
ransomware, have the highest financial impact. These insights have significant
implications for real-world cybersecurity practices, highlighting the
importance of machine learning in ransomware detection and mitigation. Further
research is recommended to expand datasets, explore alternative detection
methods, and address limitations in current approaches.
