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Research Conference on Metadata and Semantics Research

MTSR 2019: Metadata and Semantic Research pp 194–199Cite as

Ontology Based Anomaly Detection for File Integration

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1057))

Abstract

File integration systems enable file transfers between different systems in order to automate routine business processes. Therefore, the standardization in data exchange between different organizations or decentralized subsidiaries of an organization is achieved. However, abnormal situations may occur during the file integration process. In order to protect the persistence of integration channels, the abnormal files must be detected. For this purpose, anomaly detection is used to trace integrations continuously and to detect abnormal files instantly. In this study, an ontology based anomaly detection approach is proposed in order to detect abnormal situations in real time file integration systems. Thus, a file integration that is achieved on an electronic system will be traced and information will be given to the system administrator if any abnormalities occur during the integration process. Therefore, an abnormal situation that can stop the current file flow on file integration systems will be detected.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Ege University, 35100, Bornova-Izmir, Turkey

    Özgü Can & İbrahim Uzum

Authors
  1. Özgü Can
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  2. İbrahim Uzum
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Corresponding author

Correspondence to Özgü Can .

Editor information

Editors and Affiliations

  1. International Hellenic University, Thessaloniki, Greece

    Emmanouel Garoufallou

  2. Guglielmo Marconi University, Rome, Italy

    Francesca Fallucchi

  3. Georg Eckert Institute – Leibniz Institute for International Textbook Research, Braunschweig, Germany

    Ernesto William De Luca

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Can, Ö., Uzum, İ. (2019). Ontology Based Anomaly Detection for File Integration. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds) Metadata and Semantic Research. MTSR 2019. Communications in Computer and Information Science, vol 1057. Springer, Cham. https://doi.org/10.1007/978-3-030-36599-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-36599-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36598-1

  • Online ISBN: 978-3-030-36599-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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