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    Detecting cyber-attacks using a CRPS-based monitoring approach

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    Type
    Conference Paper
    Authors
    Harrou, Fouzi cc
    Bouyeddou, Benamar
    Sun, Ying cc
    Kadri, Benamar
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Environmental Statistics Group
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2019-02-28
    Online Publication Date
    2019-02-28
    Print Publication Date
    2018-11
    Permanent link to this record
    http://hdl.handle.net/10754/631694
    
    Metadata
    Show full item record
    Abstract
    Cyber-attacks can seriously affect the security of computers and network systems. Thus, developing an efficient anomaly detection mechanism is crucial for information protection and cyber security. To accurately detect TCP SYN flood attacks, two statistical schemes based on the continuous ranked probability score (CRPS) metric have been designed in this paper. Specifically, by integrating the CRPS measure with two conventional charts, Shewhart and the exponentially weighted moving average (EWMA) charts, novel anomaly detection strategies were developed: CRPS-Shewhart and CRPS-EWMA. The efficiency of the proposed methods has been verified using the 1999 DARPA intrusion detection evaluation datasets.
    Citation
    Harrou F, Bouyeddou B, Sun Y, Kadri B (2018) Detecting cyber-attacks using a CRPS-based monitoring approach. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2018.8628797.
    Sponsors
    The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST)Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The anthors(Benamar Bouyeddou and Benamar Kadri) would like to thank the STIC Lab, Department of Telecommunications, Abou Bekr Belkaid University for the continued support during the research.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2018 IEEE Symposium Series on Computational Intelligence (SSCI)
    Conference/Event name
    8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
    DOI
    10.1109/SSCI.2018.8628797
    Additional Links
    https://ieeexplore.ieee.org/document/8628797
    ae974a485f413a2113503eed53cd6c53
    10.1109/SSCI.2018.8628797
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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