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GraphSense – Realtime Anomaly Detection in Virtual and Non-Virtual Currency Networks

Insight into Digital Currencies. Virtual currency systems such as Bitcoin are becoming increasingly popular. Austrian researchers are working on algorithmic solutions that should help to understand and detect anomalies in such systems.

Short Description

The raise of digital currencies such as Bitcoin is an indicator for the ongoing digital transformation in financial technologies. Currency units are being generated decentralized and can be transferred globally within minutes and with minimal transaction costs.

In contrast to existing currency systems, virtual currencies are operating without central control (e.g., national banks) and bypass established payment processors (e.g., banks). All transactions, which have ever been executed with Bitcoin, are anonymous and accessible in the publicly visible blockchain and can therefore be accessed for analytics tasks.

The GraphSense project aims at developing algorithmic solutions for real-time analytics of virtual currency transactions, which should provide insight into functionality and transaction processes. A special focus lies on anomaly detection, which should identify transactions and transaction patterns that deviate from typical structures.

This could, for instance, help in identifying and tracing fraudulent activities. The specific characteristic and scientific challenge of the GraphSense project lies in the structure and volume of the transaction data to be analyzed. More than 100M atomic transactions form a network in which bitcoin addresses and transactions are represented by hundreds of millions nodes and edges. Anomaly detection algorithms operating on such structures must be built for horizontally scalable infrastructures (e.g., Apache Spark) and tested for their applicability.

Technologies developed within the GraphSense project should, besides the use case "Anomaly detection in virtual currencies", also be applicable for other application areas (e.g., fault detection in manufacturing processes, anomaly detection in energy networks). All developed components will therefore be published as open source software.

Project Partners

  • AIT – Austrian Institut of Technology GmbH
  • Braintribe IT Technologies GmbH
  • Wirtschaftsuniversität Wien

Contact Address

Dr. Bernhard Haslhofer
E-mail: bernhard.haslhofer@ait.ac.at

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