  The temporal nature of modeling accounts as nodes and transactions as
directed edges in a directed graph -- for a blockchain, enables us to
understand the behavior (malicious or benign) of the accounts. Predictive
classification of accounts as malicious or benign could help users of the
permissionless blockchain platforms to operate in a secure manner. Motivated by
this, we introduce temporal features such as burst and attractiveness on top of
several already used graph properties such as the node degree and clustering
coefficient. Using identified features, we train various Machine Learning (ML)
algorithms and identify the algorithm that performs the best in detecting which
accounts are malicious. We then study the behavior of the accounts over
different temporal granularities of the dataset before assigning them malicious
tags. For Ethereum blockchain, we identify that for the entire dataset - the
ExtraTreesClassifier performs the best among supervised ML algorithms. On the
other hand, using cosine similarity on top of the results provided by
unsupervised ML algorithms such as K-Means on the entire dataset, we were able
to detect 554 more suspicious accounts. Further, using behavior change analysis
for accounts, we identify 814 unique suspicious accounts across different
temporal granularities.
