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    Towards Adaptive Big Data Cyber-attack Detection via Semantic Link Networks

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    2016-TowardsAdaptiveBigDataCyber-attackDetectionviaSemanticLinkNetworks.pdf (225.7Kb)
    Permanent Link
    http://hdl.handle.net/11603/11349
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    • UMBC Faculty Collection
    • UMBC Information Systems Department
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    Metadata
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    Author/Creator
    Karabatis, George
    Wang, Jianwu
    AlEroud, Ahmed
    Date
    2016-07
    Type of Work
    5 pages
    Text
    conference paper pre-print
    Citation of Original Publication
    George Karabatis, and Jianwu Wang, and Ahmed AlEroud, Towards Adaptive Big Data Cyber-attack Detection via Semantic Link Networks, The first Workshop of Mission-Critical Big Data Analytics (MCBDA), 2016.
    Rights
    This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
    Subjects
    Adaptive Cyber-attack Detection
    Semantic Link Network
    Big Data Platform
    Streaming Data Analysis
    High Performance Computing Facilty (HPCF)
    Abstract
    As a core mechanism for cybersecurity, the ability to detect cyber-attacks is increasingly critical nowadays. There have been many types of network intrusion detection approaches, such as flow-based and packet-based, targeting single attack and multistage attack detection. Each approach has its own advantages and disadvantages. In this paper, we design an organic combination of these types of efforts into one comprehensive system. Furthermore, to deal with increasing volumes of network traffic and improve full packet analysis efficiency, we employ Spark Streaming platform for parallel detection.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.