Conference paper Open Access

Operational Data Based Intrusion Detection System for Smart Grid

Georgios Efstathopoulos; Panagiotis Radoglou Grammatikis; Panagiotis Sarigiannidis; Vasilis Argyriou; Antonios Sarigiannidis; Konstantinos Stamatakis; Michail K. Angelopoulos; Solon K. Athanasopoulos


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  <identifier identifierType="URL">https://zenodo.org/record/3834769</identifier>
  <creators>
    <creator>
      <creatorName>Georgios Efstathopoulos</creatorName>
      <affiliation>0INF, Imperial Offices, London, UK, E6 2JG</affiliation>
    </creator>
    <creator>
      <creatorName>Panagiotis Radoglou Grammatikis</creatorName>
      <affiliation>Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani 50100, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Panagiotis Sarigiannidis</creatorName>
      <affiliation>Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani 50100, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Vasilis Argyriou</creatorName>
    </creator>
    <creator>
      <creatorName>Antonios Sarigiannidis</creatorName>
      <affiliation>Sidroco Holdings Ltd, 3113, Limassol, Cyprus</affiliation>
    </creator>
    <creator>
      <creatorName>Konstantinos Stamatakis</creatorName>
      <affiliation>Testing Research &amp; Standards Center / Public Power Corporation SA, Leontariou 9, Kantza, 15351, Athens, Attica</affiliation>
    </creator>
    <creator>
      <creatorName>Michail K. Angelopoulos</creatorName>
      <affiliation>Department of Economics, University of Piraeus and the Testing Research &amp; Standards Center / Public Power Corporation SA, Leontariou 9, Kantza, 15351, Athens, Attica</affiliation>
    </creator>
    <creator>
      <creatorName>Solon K. Athanasopoulos</creatorName>
      <affiliation>Testing Research &amp; Standards Center / Public Power Corporation SA, Leontariou 9, Kantza, 15351, Athens, Attica</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Operational Data Based Intrusion Detection System for Smart Grid</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-10-07</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3834769</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/CAMAD.2019.8858503</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020_spear_project</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;With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/787011/">787011</awardNumber>
      <awardTitle>SPEAR: Secure and PrivatE smArt gRid</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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