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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      <creatorName>Christiana Ioannou</creatorName>
      <affiliation>Department of Computer Science, University of Cyprus Nicosia, Cyprus</affiliation>
      <creatorName>Vasos Vassiliou</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0001-8647-0860</nameIdentifier>
      <affiliation>Department of Computer Science, University of Cyprus Nicosia, Cyprus</affiliation>
    <title>An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression</title>
    <subject>Wireless Sensor Networks</subject>
    <subject>Internet of Things</subject>
    <subject>Intrusion Detection Systems</subject>
    <subject>Binary Logistic Regression</subject>
    <date dateType="Issued">2018-12-31</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3242102.3242145</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf"></relatedIdentifier>
  <version>Accepted pre-print</version>
    <rights rightsURI="">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&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;</description>
    <description descriptionType="Other">This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement  No 739578 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:, 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: .</description>
    <description descriptionType="Other">{"references": ["Felix B\u00fcsching, Ulf Kulau, and Lars Wolf. 2011. Demo: INGA - An Inexpensive Node for General Applications. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys '11). ACM, Seattle, WA, USA, 2.", "A. P. R. da Silva, M. H. T. Martins, B. P. S. Rocha, A. A. F. Loureiro, L. B. Ruiz, and H. C. Wong. 2005. Decentralized Intrusion Detection in Wireless Sensor Networks. In Proceedings of the 1st ACM International Workshop on Quality of Service &amp;Amp; Security in Wireless and Mobile Networks (Q2SWinet '05). ACM, New York, NY, USA, 16\u201323.", "A. Dunkels, J. Eriksson, N. Finne, and N. Tsiftes. 2011. Powertrace: Network-Level Power Profiling for Low-power Wireless Networks. Technical Report. Swedish Institute of Computer Science.", "C. Ioannou and V. Vassiliou. 2016. The Impact of Network Layer Attacks in Wireless Sensor Networks. In International Workshop on Secure Internet of Things (SIoT 2016). Crete, Greece.", "C. Ioannou, V. Vassiliou, and C. Sergiou. 2016. RMT: A Wireless Sensor Network Monitoring Tool. In Proceedings of the 13th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, &amp; Ubiquitous Networks (PE-WASUN '16). ACM, New York, NY, USA.", "C. Ioannou, V. Vassiliou, and C. Sergiou. 2017. An Intrusion Detection System for Wireless Sensor Networks. In 2017 24rd International Conference on Telecommunications (ICT).", "MAXFOR Technology Inc 2011. Sensor Network Makes Sensational World. MAXFOR Technology Inc.", "Moteiv Corporation 2006. Tmote Sky Ultra Low Power IEEE 802.15.4 compliant wireless sensor module. Moteiv Corporation", "Fredrik \u00d6sterlind. 2011. Improving Low-Power Wireless Protocols With Timing- Accurate Simulation. (2011).", "S. Raza, L. Wallgren, and T. Voigt. 2013. SVELTE: Real-time Intrusion Detection in the Internet of Things. Ad hoc networks 11, 8 (2013), 2661\u20132674.", "US-CERT. 2016. Alert (TA16-288A) Heightened DDoS Threat Posed by Mirai and Other Botnets.", "J. Zhou, Z. Cao, X. Dong, and A. V. Vasilakos. 2017. Security and Privacy for Cloud-Based IoT: Challenges. IEEE Communications Magazine 55 (2017)."]}</description>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/739578/">739578</awardNumber>
      <awardTitle>Research Center on Interactive Media, Smart System and Emerging Technologies</awardTitle>
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