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  <doc>
    <id>22451</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>433</pageFirst>
    <pageLast>438</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway, NJ</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-11-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Attack and Fault Detection in Process Control Communication Using Unsupervised Machine Learning</title>
    <parentTitle language="eng">IEEE 16th International Conference on Industrial Informatics (INDIN), Porto, Portugal, 18-20 July 2018</parentTitle>
    <identifier type="isbn">978-1-5386-4829-2</identifier>
    <identifier type="doi">10.1109/INDIN.2018.8472054</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Franka</firstName>
      <lastName>Schuster</lastName>
    </author>
    <submitter>
      <firstName>Katrin</firstName>
      <lastName>Willhöft</lastName>
    </submitter>
    <author>
      <firstName>Fabian Malte</firstName>
      <lastName>Kopp</lastName>
    </author>
    <author>
      <firstName>Andreas</firstName>
      <lastName>Paul</lastName>
    </author>
    <author>
      <firstName>Hartmut</firstName>
      <lastName>König</lastName>
    </author>
    <collection role="institutes" number="1203">FG Rechnernetze und Kommunikationssysteme</collection>
  </doc>
  <doc>
    <id>22452</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
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    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway, NJ</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-11-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Catching Intrusions: Classifier Performances for Detecting Network-specific Anomalies in Energy Systems</title>
    <parentTitle language="eng">2018 International Conference on Smart Energy Systems and Technologies (SEST), Seville, Spain, September 2018</parentTitle>
    <identifier type="isbn">978-1-5386-5326-5</identifier>
    <identifier type="doi">10.1109/SEST.2018.8495702</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Franka</firstName>
      <lastName>Schuster</lastName>
    </author>
    <submitter>
      <firstName>Katrin</firstName>
      <lastName>Willhöft</lastName>
    </submitter>
    <author>
      <firstName>Andreas</firstName>
      <lastName>Paul</lastName>
    </author>
    <author>
      <firstName>Fabian Malte</firstName>
      <lastName>Kopp</lastName>
    </author>
    <author>
      <firstName>Hartmut</firstName>
      <lastName>König</lastName>
    </author>
    <collection role="institutes" number="1203">FG Rechnernetze und Kommunikationssysteme</collection>
  </doc>
  <doc>
    <id>29953</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
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    <issue/>
    <volume/>
    <type>conferenceobject_ref</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-01-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Data Volume Reduction for Deep Packet Inspection by Multi-layer Application Determination</title>
    <abstract language="eng">Attack detection in enterprise networks is increasingly faced with large data volumes, in part high data bursts, and heavily fluctuating data flows that often cause arbitrary discarding of data packets in overload situations which can be used by attackers to hide attack activities. Attack detection systems usually configure a comprehensive set of signatures for known vulnerabilities in different operating systems, protocols, and applications. Many of these signatures, however, are not relevant in each context, since certain vulnerabilities have already been eliminated, or the vulnerable applications or operating system versions, respectively, are not installed on the involved systems. In this paper, we present an approach for clustering data flows to assign them to dedicated analysis units that contain only signature sets relevant for the analysis of these flows. We discuss the performance of this clustering and show how it can be used in practice to improve the efficiency of an analysis pipeline.</abstract>
    <parentTitle language="deu">2022 IEEE International Conference on Cyber Security and Resilience (CSR), 27-29 July 2022, Rhodes, Greece</parentTitle>
    <identifier type="url">https://ieeexplore.ieee.org/abstract/document/9850293</identifier>
    <identifier type="doi">10.1109/CSR54599.2022.9850293</identifier>
    <identifier type="isbn">978-1-6654-9952-1</identifier>
    <identifier type="isbn">978-1-6654-9953-8</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Artikelnummer">INSPEC Accession Number: 21971135</enrichment>
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    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Michael</firstName>
      <lastName>Vogel</lastName>
    </author>
    <submitter>
      <firstName>Katrin</firstName>
      <lastName>Willhöft</lastName>
    </submitter>
    <author>
      <firstName>Franka</firstName>
      <lastName>Schuster</lastName>
    </author>
    <author>
      <firstName>Fabian Malte</firstName>
      <lastName>Kopp</lastName>
    </author>
    <author>
      <firstName>Hartmut</firstName>
      <lastName>König</lastName>
    </author>
    <collection role="institutes" number="1205">FG IT-Sicherheit</collection>
  </doc>
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