  Blockchain networks are critical for safeguarding digital transactions and
assets, but they are increasingly targeted by ransomware attacks exploiting
zero-day vulnerabilities. Traditional detection techniques struggle due to the
complexity of these exploits and the lack of comprehensive datasets. The
UGRansome dataset addresses this gap by offering detailed features for
analysing ransomware and zero-day attacks, including timestamps, attack types,
protocols, network flows, and financial impacts in bitcoins (BTC). This study
uses the Lazy Predict library to automate machine learning (ML) on the
UGRansome dataset. The study aims to enhance blockchain security through
ransomware detection based on zero-day exploit recognition using the UGRansome
dataset. Lazy Predict streamlines different ML model comparisons and identifies
effective algorithms for threat detection. Key features such as timestamps,
protocols, and financial data are used to predict anomalies as zero-day threats
and to classify known signatures as ransomware. Results demonstrate that ML can
significantly improve cybersecurity in blockchain environments. The
DecisionTreeClassifier and ExtraTreeClassifier, with their high performance and
low training times, are ideal candidates for deployment in real-time threat
detection systems.
