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    A Wireless Intrusion Detection for the Next Generation (5G) Networks

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    Ferrucci_5G Wireless Security.pdf (395.0Kb)
    Date
    2020-05
    Author
    Ferrucci, Richard
    Kholidy, Hisham A.; Advisor
    Publisher
    SUNY Polytechnic Institute
    Metadata
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    Subject
    5G systems
    cybersecurity
    intrusion detection
    machine learning
    Device2Device
    LogIT boost
    wireless systems
    Abstract
    5G data systems are closed to delivery to the public. The question remains how security will impact the release of this cutting edge architecture. 5G data systems will be sending massive amounts of personal data due to the fact that everybody in the world is using mobile phones these days. With everyone using a 5G device, this architecture will have a huge surface area for attackers to compromise. Using machine learning techniques previously applied to 802.11 networks. We will show that improving upon these previous works, we can have a better handle on security when it comes to 5G architecture security. We find that using a machine learning classifier known as LogIT boost, combined with a selected combination of feature selection, we can provide optimal results in identifying three different classes of traffic referred to as normal, flooding, and injection traffic. We drastically decrease the time taken to perform this classification while improving the results. We simulate the Device2Device (D2D) connections involved in the 5G systems using the AWID dataset. The evaluation and validation of the classification approach are discussed in details in this thesis.
    URI
    http://hdl.handle.net/1951/71329
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    • College of Engineering, SUNY Polytechnic Institute [44]

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