  The rise of ransomware attacks has necessitated the development of effective
strategies for identifying and mitigating these threats. This research
investigates the utilization of a feature selection algorithm for
distinguishing ransomware-related and benign transactions in both Bitcoin (BTC)
and United States Dollar (USD). Leveraging the UGRansome dataset, a
comprehensive repository of ransomware related BTC and USD transactions, we
propose a set of novel features designed to capture the distinct
characteristics of ransomware activity within the cryptocurrency ecosystem.
These features encompass transaction metadata, ransom analysis, and behavioral
patterns, offering a multifaceted view of ransomware-related financial
transactions. Through rigorous experimentation and evaluation, we demonstrate
the effectiveness of our feature set in accurately extracting BTC and USD
transactions, thereby aiding in the early detection and prevention of
ransomware-related financial flows. We introduce a Ransomware Feature Selection
Algorithm (RFSA) based on Gini Impurity and Mutual Information (MI) for
selecting crucial ransomware features from the UGRansome dataset. Insights from
the visualization highlight the potential of Gini Impurity and MI-based feature
selection to enhance ransomware detection systems by effectively discriminating
between ransomware classes. The analysis reveals that approximately 68% of
ransomware incidents involve BTC transactions within the range of 1.46 to 2.56,
with an average of 2.01 BTC transactions per attack. The findings emphasize the
dynamic and adaptable nature of ransomware demands, suggesting that there is no
fixed amount for specific cyberattacks, highlighting the evolving landscape of
ransomware threats.
