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<p class="MsoNormal" style="margin-bottom: 10.0pt; line-height: 115%;"><span lang="EN-US" style="font-size: 11.0pt; line-height: 115%; mso-fareast-font-family: 'PT Sans'; mso-ansi-language: EN-US;">This book presents a proof of concept architecture to detect and prevent Sybil attacks in automated Machine-to-Machine networks. Designed in regards to low-powered Internet of Things devices, the proposed layered defence balances security with computational feasibility, using Distributed Ledger Technology and Multi-Agent Systems. The thesis begins with the theoretical background of the used technologies and related work, which the methodology is based on. Using the Design Science Research framework, the methodology explains the process of building the simulation, its layers and the combined approach, followed by the results which are compared and discussed. The results show that a multi-layered approach reduces false positives and offers a more balanced detection framework compared to the isolated methods. The thesis concludes by discussing the framework's scalability, limitations and future research directions for securing decentralized payment systems.</span></p>

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Securing Peer-to-Peer Identities in IoT Systems Using Blockchain and Multi-Agent Systems with an Outlook on M2M Payment Applications

  • 书籍

  • 发表日期：

    2026年

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  • Karen Ayu Stiller

摘要

This book presents a proof of concept architecture to detect and prevent Sybil attacks in automated Machine-to-Machine networks. Designed in regards to low-powered Internet of Things devices, the proposed layered defence balances security with computational feasibility, using Distributed Ledger Technology and Multi-Agent Systems. The thesis begins with the theoretical background of the used technologies and related work, which the methodology is based on. Using the Design Science Research framework, the methodology explains the process of building the simulation, its layers and the combined approach, followed by the results which are compared and discussed. The results show that a multi-layered approach reduces false positives and offers a more balanced detection framework compared to the isolated methods. The thesis concludes by discussing the framework's scalability, limitations and future research directions for securing decentralized payment systems.