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
2019Supervisor
Abd-Alhameed, Raed A.Shepherd, Simon J.
Mantas, G.
Keyword
SecurityIntrusion detection
Android
5G
Prevention
Host-based
Malware detection
Host-based IDS
Statistical anomaly detection
Machine learningHIDROID (Host-based Intrusion Detection and protection system for andROID)
Rights
The University of Bradford theses are licenced under a Creative Commons Licence.
Institution
University of BradfordDepartment
School of Engineering, Design and TechnologyAwarded
2019Metadata
Show full item recordAbstract
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.Type
ThesisQualification name
PhDCollections
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