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An analysis of the Gateway Integrity Checking Protocol from the perspective of Intrusion Detection Systems

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Abstract

Internet of Things (IoT) gateways bridging resource-constrained devices and the internet, running conventional operating systems and communication protocols, are valuable targets for malicious attackers. Once compromised, the gateway can no longer be trusted to deliver data accurately to the applications running on the Cloud. In this work we present an analysis of the Gateway Integrity Checking Protocol, a gossip protocol that collects data from subsets of IoT devices, to answer a challenge sent by an External Security Agent. The response is used to check the integrity of data arriving at the Cloud from the gateway. The evaluation of the time and energy overhead, and the impact of the proposed solution on the lifetime of the network through simulations is presented. A detailed qualitative comparison against other Intrusion Detection Systems in the IoT domain is made, showing that the proposed solution can provide customizable security levels with little overhead in terms of processing power and network traffic.

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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Authors and Affiliations

  1. Software/Hardware Integration Lab - Federal University of Santa Catarina, Florianópolis, Joinville, Brazil

    Mateus Martínez de Lucena & Antônio Augusto Fröhlich

  2. Federal University of Technology - Paraná, Toledo, Brazil

    Roberto Milton Scheffel

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  1. Mateus Martínez de Lucena
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  2. Roberto Milton Scheffel
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  3. Antônio Augusto Fröhlich
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Correspondence to Mateus Martínez de Lucena.

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This research was partially funded by CAPES and by CNPq, Brazil.

This paper was originally published in the 2019 IX Brazilian Symposium on Computing Systems Engineering (SBESC) [1].

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Martínez de Lucena, M., Scheffel, R.M. & Fröhlich, A.A. An analysis of the Gateway Integrity Checking Protocol from the perspective of Intrusion Detection Systems. Des Autom Embed Syst 25, 89–111 (2021). https://doi.org/10.1007/s10617-020-09240-8

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