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  • Detecting Intrusions within RFID Systems through Non-Monotonic Reasoning Cleaning

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    67460_1.pdf (214.8Kb)
    Author(s)
    Darcy, P
    Stantic, B
    Mitrokotsa, A
    Sattar, A
    Griffith University Author(s)
    Stantic, Bela
    Sattar, Abdul
    Darcy, Peter J.
    Year published
    2010
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    Abstract
    Radio Frequency Identification (RFID) technology uses wireless radio technology to automatically track items spanning various locations with little effort or cost. It is used for various applications such as supply chains and health care. Unfortunately, due to the nature of the passive RFID architecture, cloned tags lower the overall accuracy of the system and may allow unauthorised personnel access to sensitive areas/information. To combat this considerable threat, we have proposed a cleaning algorithm designed specifically to identify and flag potential intrusions utilising intelligent analysis coupled with Non-Monotonic ...
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    Radio Frequency Identification (RFID) technology uses wireless radio technology to automatically track items spanning various locations with little effort or cost. It is used for various applications such as supply chains and health care. Unfortunately, due to the nature of the passive RFID architecture, cloned tags lower the overall accuracy of the system and may allow unauthorised personnel access to sensitive areas/information. To combat this considerable threat, we have proposed a cleaning algorithm designed specifically to identify and flag potential intrusions utilising intelligent analysis coupled with Non-Monotonic Reasoning. We conclude this research by analysing the potential benefits and drawbacks within our proposed methodology.
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    Conference Title
    Proceedings of the 2010 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010
    DOI
    https://doi.org/10.1109/ISSNIP.2010.5706753
    Copyright Statement
    © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Subject
    Data engineering and data science
    Publication URI
    http://hdl.handle.net/10072/37315
    Collection
    • Conference outputs

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