loading page

Feature Engineering for Anomaly Detection and Classification of Blockchain Transactions
  • +3
  • Samantha Jeyakumar ,
  • Eugene Yugarajah , Andrew Charles ,
  • Punit Rathore ,
  • Vallipuram Muthukkumarasamy ,
  • Zhé Hóu
Samantha Jeyakumar
Author Profile
Eugene Yugarajah , Andrew Charles
Author Profile
Punit Rathore
Author Profile
Author Profile
Vallipuram Muthukkumarasamy
Author Profile
Zhé Hóu
Author Profile


The study analysed the importance of blockchain transaction features to identify suspicious activities. The feature engineering process involves exploiting domain knowledge, applying intuition, and performing a time-consuming series of trial-and-error extractions. Manually overseeing this process significantly impacts the performance of model generation. We address this challenge with an automated feature engineering approach to extract the various features from blockchain transactions. Also, we engineered a set of new features based on statistical measures and graph representation. We demonstrate that the proposed approach can be applied to various blockchain transaction datasets, including Bitcoin and Ethereum. The engineered features were tested against eight classifiers, including random forest, XG-boost, Silas, and neural network-based classifiers to identify the suspicious behaviour of transactions