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Clust-IT: Clustering-Based Intrusion Detection in IoT Environments

Markiewicz, R.; Sgandurra, D.


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  <identifier identifierType="DOI">10.5281/zenodo.4146827</identifier>
  <creators>
    <creator>
      <creatorName>Markiewicz, R.</creatorName>
      <givenName>R.</givenName>
      <familyName>Markiewicz</familyName>
      <affiliation>Information Security Group, Royal Holloway</affiliation>
    </creator>
    <creator>
      <creatorName>Sgandurra, D.</creatorName>
      <givenName>D.</givenName>
      <familyName>Sgandurra</familyName>
      <affiliation>Information Security Group, Royal Holloway</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Clust-IT: Clustering-Based Intrusion Detection in IoT Environments</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-10-28</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4146827</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="Handle" relationType="IsPreviousVersionOf" resourceTypeGeneral="JournalArticle">https: //doi.org/10.1145/3407023.3409201</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4146826</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/futuretpm-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Low-powered and resource-constrained devices are forming a greater part of our smart networks. For this reason, they have recently been the target of various cyber-attacks. However, these devices often cannot implement traditional intrusion detection systems (IDS), or they can not produce or store the audit trails needed for inspection. Therefore, it is often necessary to adapt existing IDS systems and malware detection approaches to cope with these constraints. We explore the application of unsupervised learning techniques, specifically clustering, to develop a novel IDS for networks composed of low-powered devices. We describe our solution, called Clust-IT (Clustering of IoT), to manage heterogeneous data collected from cooperative and distributed networks of connected devices and searching these data for indicators of compromise while remaining protocol agnostic. We outline a novel application of OPTICS to various available IoT datasets, composed of both packet and flow captures, to demonstrate the capabilities of the proposed techniques and evaluate their feasibility in developing an IoT IDS.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/779391/">779391</awardNumber>
      <awardTitle>Future Proofing the Connected World: A Quantum-Resistant Trusted Platform Module</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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