@inproceedings{Wittek2020,
  author    = {Kevin Wittek},
  title     = {Statistical Anomaly Detection in Ethereum Transaction Graphs},
  series = {Konferenzband zum Scientific Track der Blockchain Autumn School 2020},
  publisher = {Hochschule Mittweida},
  address   = {Mittweida},
  issn      = {1437-7624},
  doi       = {10.48446/opus-11872},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mit1-opus4-118729},
  pages     = {106 -- 110},
  year      = {2020},
  abstract  = {The set of transactions that occurs on the public ledger of an Ethereum network in a specific time frame can be represented as a directed graph, with vertices representing addresses and an edge indicating the interaction between two addresses. While there exists preliminary research on analyzing an Ethereum network by the means of graph analysis, most existing work is focused on either the public Ethereum Mainnet or on analyzing the different semantic transaction layers using static graph analysis in order to carve out the different network properties (such as interconnectivity, degrees of centrality, etc.) needed to characterize a blockchain network. By analyzing the consortium-run bloxberg Proof-of-Authority (PoA) Ethereum network, we show that we can identify suspicious and potentially malicious behaviour of network participants by employing statistical graph analysis. We thereby show that it is possible to identify the potentially malicious exploitation of an unmetered and weakly secured blockchain network resource. In addition, we show that Temporal Network Analysis is a promising technique to identify the occurrence of anomalies in a PoA Ethereum network.},
  language  = {en}
}