Conference paper Open Access

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

Markiewicz, R.; Sgandurra, D.

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="">
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  <controlfield tag="005">20201104122708.0</controlfield>
  <controlfield tag="001">4222626</controlfield>
  <datafield tag="711" ind1=" " ind2=" ">
    <subfield code="d">Aug 25, 2020 – Aug 28, 2020</subfield>
    <subfield code="a">15th International ARES Conference on Availability, Reliability and Security</subfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Information Security Group, Royal Holloway</subfield>
    <subfield code="a">Sgandurra, D.</subfield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">1910186</subfield>
    <subfield code="z">md5:30df3a3da01997b07a89a305ef3359d0</subfield>
    <subfield code="u"></subfield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-10-28</subfield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="p">user-futuretpm-h2020</subfield>
    <subfield code="o"></subfield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Information Security Group, Royal Holloway</subfield>
    <subfield code="a">Markiewicz, R.</subfield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Clust-IT: Clustering-Based Intrusion Detection in IoT Environments</subfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-futuretpm-h2020</subfield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">779391</subfield>
    <subfield code="a">Future Proofing the Connected World: A Quantum-Resistant Trusted Platform Module</subfield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u"></subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2"></subfield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&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;</subfield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">handle</subfield>
    <subfield code="i">isPreviousVersionOf</subfield>
    <subfield code="a">https: //</subfield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.4146826</subfield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.4222626</subfield>
    <subfield code="2">doi</subfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">conferencepaper</subfield>
All versions This version
Views 9823
Downloads 12183
Data volume 232.9 MB158.5 MB
Unique views 9121
Unique downloads 11582


Cite as