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    Evaluating Variant Deep Learning and Machine Learning Approaches for the Detection of Cyberattacks on the Next Generation 5G Systems

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    Author
    Borgesen, Michael E.
    Kholidy, Hisham A.; Advisor
    Keyword
    machine learning approaches
    cyberattacks detection
    5G systems security
    intrusion detection systems
    performance evaluation
    telecommunication networks
    Naïve Bayes model
    logistic regression model
    decision tree model
    random forest model
    Date Published
    2020
    
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    URI
    http://hdl.handle.net/20.500.12648/1597
    Abstract
    5G technology promises to completely transform telecommunication networks, introducing a wealth of benefits such as faster download speeds, lower download times, low latency, high network capacity. These benefits will pave the way for additional new capabilities and support connectivity for applications like smart homes and cities, industrial automation, autonomous vehicles, telemedicine, and virtual/augmented reality. However, attackers use these resources in their advantages to speed up the attacking process. This report evaluates four different machine learning and deep learning approaches namely the Naïve Bayes model, the logistic regression model, the decision tree model, and the random forest model. The performance evaluation and the validation of these approaches are discussed in details in this report.
    Citation
    Borgesen, M. L., & Kholidy, H. A. (2020). Evaluating Variant Deep Learning and Machine Learning Approaches for the Detection of Cyberattacks on the Next Generation 5G Systems. SUNY Polytechnic Institute, College of Engineering.
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