BAD: A blockchain anomaly detection solution

Matteo Signorini, Matteo Pontecorvi, Waël Kanoun, Roberto Di Pietro*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Anomaly detection tools play a role of paramount importance in protecting networks and systems from unforeseen attacks, usually by automatically recognizing and filtering out anomalous activities. Over the years, different approaches have been designed, all focused on lowering the false positive rate. However, no proposal has addressed attacks specifically targeting blockchain-based systems. In this paper, we present BAD: Blockchain Anomaly Detection. This is the first solution, to the best of our knowledge, that is tailored to detect anomalies in blockchain-based systems. BAD is a complete framework, relying on several components leveraging, at its core, blockchain meta-data in order to collect potentially malicious activities. BAD enjoys some unique features: (i) it is distributed (thus avoiding any central point of failure); (ii) it is tamper-proof (making it impossible for a malicious software to remove or to alter its own traces); (iii) it is trusted (any behavioral data is collected and verified by the majority of the network); and, (iv) it is private (avoiding any third party to collect/analyze/store sensitive information). Our proposal is described in detail and validated via both experimental results and analysis, that highlight the quality and viability of our Blockchain Anomaly Detection solution.

Original languageEnglish (US)
Pages (from-to)173481-173490
Number of pages10
JournalIEEE Access
StatePublished - 2020

Bibliographical note

Funding Information:
The authors would like to thank the anonymous reviewers for their suggestions, that helped to improve the quality of the manuscript. The publication of this article was funded by the Qatar National Library (QNL), Doha, Qatar and award NPRP11S-0109-180242 from the Qatar National Research Fund (QNRF), member of Qatar Foundation. The information and views set out in this publication are those of the authors and do not necessarily reflect the official opinion of QNL and QNRF.

Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.


  • Blockchain technology
  • Distributed systems
  • Intrusion detection systems
  • Security

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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