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An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression

Christiana Ioannou; Vasos Vassiliou


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    <subfield code="a">This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2)  and the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.

© ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of MSWiM 2018, DOI: https://doi.org/10.1145/3242102.3242145, Christiana Ioannou and Vasos Vassiliou. 2018. An Intrusion Detection System for Constrained WSN and IoT. In the Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems  Montreal(MSWIM '18), QC, Canada — October 28 - November 02, 2018. ACM, New York, NY, USA, 259-263. DOI:  https://doi.org/10.1145/3242102. https://www.acm.org/publications/policies/copyright-policy .</subfield>
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    <subfield code="a">Vasos Vassiliou</subfield>
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    <subfield code="a">An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression</subfield>
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    <subfield code="a">&lt;p&gt;In this paper we evaluate the feasibility of running a lightweight Intrusion Detection System within a constrained sensor or IoT&lt;br&gt;
node. We propose mIDS, which monitors and detects attacks using a statistical analysis tool based on Binary Logistic Regression (BLR). mIDS takes as input only local node parameters for both benign and malicious behavior and derives a normal behavior model that detects abnormalities within the constrained node.We offer a proof of correct operation by testing mIDS in a setting where network-layer attacks are present. In such a system, critical data from the routing layer is obtained and used as a basis for profiling sensor behavior. Our results show that, despite the lightweight implementation, the proposed solution achieves attack detection accuracy levels within the range of 96% - 100%.&lt;/p&gt;</subfield>
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