Detecting Financial Bots on the Ethereum Blockchain
Thomas Niedermayer Pietro Saggese Bernhard Haslhofer
The integration of bots in Distributed Ledger Technologies (DLTs) fosters
efficiency and automation. However, their use is also associated with predatory
trading and market manipulation, and can pose threats to system integrity. It
is therefore essential to understand the extent of bot deployment in DLTs;
despite this, current detection systems are predominantly rule-based and lack
flexibility. In this study, we present a novel approach that utilizes machine
learning for the detection of financial bots on the Ethereum platform. First,
we systematize existing scientific literature and collect anecdotal evidence to
establish a taxonomy for financial bots, comprising 7 categories and 24
subcategories. Next, we create a ground-truth dataset consisting of 133 human
and 137 bot addresses. Third, we employ both unsupervised and supervised
machine learning algorithms to detect bots deployed on Ethereum. The
highest-performing clustering algorithm is a Gaussian Mixture Model with an
average cluster purity of 82.6%, while the highest-performing model for binary
classification is a Random Forest with an accuracy of 83%. Our machine
learning-based detection mechanism contributes to understanding the Ethereum
ecosystem dynamics by providing additional insights into the current bot
landscape.