License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.Tokenomics.2020.8
URN: urn:nbn:de:0030-drops-135306
URL: https://drops.dagstuhl.de/opus/volltexte/2021/13530/
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Penzkofer, Andreas ; Kusmierz, Bartosz ; Capossele, Angelo ; Sanders, William ; Saa, Olivia

Parasite Chain Detection in the IOTA Protocol

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OASIcs-Tokenomics-2020-8.pdf (0.5 MB)


Abstract

In recent years several distributed ledger technologies based on directed acyclic graphs (DAGs) have appeared on the market. Similar to blockchain technologies, DAG-based systems aim to build an immutable ledger and are faced with security concerns regarding the irreversibility of the ledger state. However, due to their more complex nature and recent popularity, the study of adversarial actions has received little attention so far. In this paper we are concerned with a particular type of attack on the IOTA cryptocurrency, more specifically a Parasite Chain attack that attempts to revert the history stored in the DAG structure, also called the Tangle.
In order to improve the security of the Tangle, we present a detection mechanism for this type of attack. In this mechanism, we embrace the complexity of the DAG structure by sampling certain aspects of it, more particularly the distribution of the number of approvers. We initially describe models that predict the distribution that should be expected for a Tangle without any malicious actors. We then introduce metrics that compare this reference distribution with the measured distribution. Upon detection, measures can then be taken to render the attack unsuccessful. We show that due to a form of the Parasite Chain that is different from the main Tangle it is possible to detect certain types of malicious chains. We also show that although the attacker may change the structure of the Parasite Chain to avoid detection, this is done so at a significant cost since the attack is rendered less efficient.

BibTeX - Entry

@InProceedings{penzkofer_et_al:OASIcs.Tokenomics.2020.8,
  author =	{Andreas Penzkofer and Bartosz Kusmierz and Angelo Capossele and William Sanders and Olivia Saa},
  title =	{{Parasite Chain Detection in the IOTA Protocol}},
  booktitle =	{2nd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2020)},
  pages =	{8:1--8:18},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-157-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{82},
  editor =	{Emmanuelle Anceaume and Christophe Bisi\`{e}re and Matthieu Bouvard and Quentin Bramas and Catherine Casamatta},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/13530},
  URN =		{urn:nbn:de:0030-drops-135306},
  doi =		{10.4230/OASIcs.Tokenomics.2020.8},
  annote =	{Keywords: Distributed ledger technology, cryptocurrency, directed acyclic graph, security, attack detection algorithm}
}

Keywords: Distributed ledger technology, cryptocurrency, directed acyclic graph, security, attack detection algorithm
Collection: 2nd International Conference on Blockchain Economics, Security and Protocols (Tokenomics 2020)
Issue Date: 2021
Date of publication: 02.02.2021


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