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    • 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)
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    • 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)
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    An advanced adaptive learning intrusion prevention system

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    ΑΥΔ-2018-00024.pdf (1.223Mb)
    Date
    2018-12
    Author
    Constantinides, Chrstos
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    Abstract
    Computer and network attackers are continuously evolving their attack vectors to evade intrusion detection systems. Commercial and real-world intrusion detection prevention systems suffer with low detection rates and high false positives which require substantial optimization and network specific fine tuning. Furthermore, the majority of those systems rely on signatures to detect potential attacks and therefore unknown attacks to the public - "zero day attacks", are by definition, undetectable by such systems. Intrusion Detection Prevention Systems fail to satisfy the organizations security requirements in detecting newly published attacks or variants of existing attacks, effectively responding to attacks launched by sophisticated attackers and resisting attacks that are intended to circumvent them. This is the result of Intrusion Detection Prevention Systems lack of adaptation to new information. Introducing "intelligence" to Intrusion Detection Prevention Systems could solve the problems mentioned above. This thesis propose a novel Network Intrusion Prevention System that utilizes Self Organizing Incremental Neural Networks along with SVMs, not relying on signatures or rules and capable to mitigate known and unknown attacks on a high accurate level in an "online" and incremental manner. Based on the experimental results with NSL KDD dataset the proposed framework can achieve on-line updated incremental learning, suitable for efficient and scaling industrial applications with high accuracy results.
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    http://hdl.handle.net/11128/3931
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    • Ασφάλεια Υπολογιστών και Δικτύων (ΕΛΛ) / Computer and Network Security (in Greek)

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    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