Kypseli Logo
    • Ελληνικά
    • English
  •  Home
  •  Browse 
    • Communities & Collections
    • By Issue Date
    • Authors
    • Titles
    • Subjects
    • By Issue number
  • Language elLanguage en
  •  Login 
    • Sign in
    View Item 
    • Home
    • Αποθετήριο Ανοικτού Πανεπιστημίου Κύπρου (Repository of the Open University of Cyprus)
    • Μεταπτυχιακές διατριβές / Master Τhesis
    • Ασφάλεια Υπολογιστών και Δικτύων (ΕΛΛ) / Computer and Network Security (in Greek)
    • View Item
    •   Home
    • Αποθετήριο Ανοικτού Πανεπιστημίου Κύπρου (Repository of the Open University of Cyprus)
    • Μεταπτυχιακές διατριβές / Master Τhesis
    • Ασφάλεια Υπολογιστών και Δικτύων (ΕΛΛ) / Computer and Network Security (in Greek)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Mitigating insider threats using bio-inspired models.

    Thumbnail
    View/Open
    ΑΥΔ-2020-00062.pdf (757.6Kb)
    Date
    2020-05
    Author
    Nicolaou, Andreas S.
    Metadata
    Show full item record
    Abstract
    Insider Threat has become a huge information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of information and network security technology, the insider threat problem is on the rise and many researchers are approaching the problem with various methods, in order to develop a model that will help organizations to reduce their exposure to the threat and prevent damage to their assets. In this M.Sc. dissertation we approach the insider threat problem and attempt to mitigate it, by developing a machine learning model based on bio-inspired computing. The model was developed by using an existing unsupervised learning algorithm for anomaly detection and we fitted the model to a synthetic dataset to detect outliers. We explored swarm intelligence algorithms and their performance on feature selection optimization for improving the performance of the machine learning model. The results showed that swarm intelligence algorithms perform well on feature selection optimization and the generated near-optimal subset of features that has similar performance with the original one.
    URI
    http://hdl.handle.net/11128/4621
    Collections
    • Ασφάλεια Υπολογιστών και Δικτύων (ΕΛΛ) / Computer and Network Security (in Greek)

    Open University of Cyprus

    PO Box 12794,

    2252, Latsia

    Cyprus

    Tel.: +357 22 411600

    Fax.: +357 22 411601

    • Help
    • Contact Us
    • Open University of Cyprus
    • OUC Library
    • Policies
    • Accessibility and Data Protection

    Find us on:

    • FacebookFacebook
    • EU Flag
    • Republic of Cyprus
    • Structural Funds
    • e University
    • Open University of Cyprus

    The eUniversity Project is co-founded by the European Regional Development Fund and National Funds in the Programmatic Period 2007-2013

     

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy Issue numberThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Issue number

    My Account

    Sign inRegister

    Open University of Cyprus

    PO Box 12794,

    2252, Latsia

    Cyprus

    Tel.: +357 22 411600

    Fax.: +357 22 411601

    • Help
    • Contact Us
    • Open University of Cyprus
    • OUC Library
    • Policies
    • Accessibility and Data Protection

    Find us on:

    • FacebookFacebook
    • EU Flag
    • Republic of Cyprus
    • Structural Funds
    • e University
    • Open University of Cyprus

    The eUniversity Project is co-founded by the European Regional Development Fund and National Funds in the Programmatic Period 2007-2013