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International Conference on Machine Learning for Networking

MLN 2018: Machine Learning for Networking pp 364–377Cite as

Detecting Suspicious Transactions in IoT Blockchains for Smart Living Spaces

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11407))

Abstract

The idea of connecting physical things and cyber components to enable new and richer interactions is a key component in any smart space concept. One of the central challenges in these new smart spaces is the access control of data, services and things. In recent years, Distributed Ledger technology (DLT) like Blockchain Technology (BCT), emerged as the most promising solution for decentralized access management. Using capability-based access control, access to data/services/things is achieved by transferring tokens between the accounts of a distributed ledger. Managing how the access tokens are transferred is, of course, a major challenge. Within the IoT space, smart contracts are at the center of most of the proposals for DLT/BCT networks targeting access control. The main problem in using smart contracts as a means for checking if and what access token can be transferred from one account to another is their immutability and accessibility. Smart contracts and chain code are by design meant to be immutable since they represent a binding contract between parties. In addition, they need to be accessible since they are to be executed on many nodes. This allows an attacker to study them and design the attack in a manner that passes the rules of the smart contract/chain code. This paper focuses on the use of metadata as a more effective means to prevent attackers from gaining access to data/services/things in a smart living space.

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Authors and Affiliations

  1. Department of Computer Science, University of Saskatchewan, Saskatoon, Canada

    Mayra Samaniego & Ralph Deters

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  1. Mayra Samaniego
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  2. Ralph Deters
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Correspondence to Ralph Deters .

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Editors and Affiliations

  1. Télécom SudParis, Évry, France

    Éric Renault

  2. Inria, Paris, France

    Paul Mühlethaler

  3. CNAM/CEDRIC, Paris, France

    Selma Boumerdassi

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Samaniego, M., Deters, R. (2019). Detecting Suspicious Transactions in IoT Blockchains for Smart Living Spaces. In: Renault, É., Mühlethaler, P., Boumerdassi, S. (eds) Machine Learning for Networking. MLN 2018. Lecture Notes in Computer Science(), vol 11407. Springer, Cham. https://doi.org/10.1007/978-3-030-19945-6_26

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  • DOI: https://doi.org/10.1007/978-3-030-19945-6_26

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