Conference paper Open Access

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

Christiana Ioannou; Vasos Vassiliou

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{
"inLanguage": {
"alternateName": "eng",
"@type": "Language",
"name": "English"
},
"description": "<p>In this paper we evaluate the feasibility of running a lightweight Intrusion Detection System within a constrained sensor or IoT<br>\nnode. 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%.</p>",
"creator": [
{
"affiliation": "Department of Computer Science, University of Cyprus Nicosia, Cyprus",
"@type": "Person",
"name": "Christiana Ioannou"
},
{
"affiliation": "Department of Computer Science, University of Cyprus Nicosia, Cyprus",
"@id": "https://orcid.org/0000-0001-8647-0860",
"@type": "Person",
"name": "Vasos Vassiliou"
}
],
"headline": "An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression",
"datePublished": "2018-12-31",
"url": "https://zenodo.org/record/2671469",
"version": "Accepted pre-print",
"keywords": [
"Wireless Sensor Networks",
"Internet of Things",
"Intrusion Detection Systems",
"Binary Logistic Regression"
],
"@context": "https://schema.org/",
"identifier": "https://doi.org/10.1145/3242102.3242145",
"@id": "https://doi.org/10.1145/3242102.3242145",
"@type": "ScholarlyArticle",
"name": "An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression"
}
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