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    An autonomous host-based intrusion detection and prevention system for Android mobile devices. Design and implementation of an autonomous host-based Intrusion Detection and Prevention System (IDPS), incorporating Machine Learning and statistical algorithms, for Android mobile devices

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    PhD Thesis (11.39Mb)
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    Publication date
    2019
    Author
    Ribeiro, José C.V.G.
    Supervisor
    Abd-Alhameed, Raed A.
    Shepherd, Simon J.
    Mantas, G.
    Keyword
    Security
    Intrusion detection
    Android
    5G
    Prevention
    Host-based
    Malware detection
    Host-based IDS
    Statistical anomaly detection
    Machine learning
    HIDROID (Host-based Intrusion Detection and protection system for andROID)
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    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    School of Engineering, Design and Technology
    Awarded
    2019
    
    Metadata
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    Abstract
    This research work presents the design and implementation of a host-based Intrusion Detection and Prevention System (IDPS) called HIDROID (Host-based Intrusion Detection and protection system for andROID) for Android smartphones. It runs completely on the mobile device, with a minimal computation burden. It collects data in real-time, periodically sampling features that reflect the overall utilisation of scarce resources of a mobile device (e.g. CPU, memory, battery, bandwidth, etc.). The Detection Engine of HIDROID adopts an anomaly-based approach by exploiting statistical and machine learning algorithms. That is, it builds a data-driven model for benign behaviour and looks for the outliers considered as suspicious activities. Any observation failing to match this model triggers an alert and the preventive agent takes proper countermeasure(s) to minimise the risk. The key novel characteristic of the Detection Engine of HIDROID is the fact that it requires no malicious data for training or tuning. In fact, the Detection Engine implements the following two anomaly detection algorithms: a variation of K-Means algorithm with only one cluster and the univariate Gaussian algorithm. Experimental test results on a real device show that HIDROID is well able to learn and discriminate normal from anomalous behaviour, demonstrating a very promising detection accuracy of up to 0.91, while maintaining false positive rate below 0.03. Finally, it is noteworthy to mention that to the best of our knowledge, publicly available datasets representing benign and abnormal behaviour of Android smartphones do not exist. Thus, in the context of this research work, two new datasets were generated in order to evaluate HIDROID.
    URI
    http://hdl.handle.net/10454/18742
    Type
    Thesis
    Qualification name
    PhD
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