Behaviour based anomaly detection system for smartphones using machine learning algorithm

Majeed, Khurram (2015) Behaviour based anomaly detection system for smartphones using machine learning algorithm. Doctoral thesis, London Metropolitan University.

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Abstract

In this research, we propose a novel, platform independent behaviour-based anomaly detection system for smartphones. The fundamental premise of this system is that every smartphone user has unique usage patterns. By modelling these patterns into a profile we can uniquely identify users. To evaluate this hypothesis, we conducted an experiment in which a data collection application was developed to accumulate real-life dataset consisting of application usage statistics, various system metrics and contextual information from smartphones. Descriptive statistical analysis was performed on our dataset to identify patterns of dissimilarity in smartphone usage of the participants of our experiment. Following this analysis, a Machine Learning algorithm was applied on the dataset to create a baseline usage profile for each participant. These profiles were compared to monitor deviations from baseline in a series of tests that we conducted, to determine the profiling accuracy. In the first test, seven day smartphone usage data consisting of eight features and an observation interval of one hour was used and an accuracy range of 73.41% to 100% was achieved. In this test, 8 out 10 user profiles were more than 95% accurate. The second test, utilised the entire dataset and achieved average accuracy of 44.50% to 95.48%. Not only these results are very promising in differentiating participants based on their usage, the implications of this research are far reaching as our system can also be extended to provide transparent, continuous user authentication on smartphones or work as a risk scoring engine for other Intrusion Detection System.

Item Type: Thesis (Doctoral)
Additional Information: uk.bl.ethos.681217
Uncontrolled Keywords: Algorithms; Computer networks -- Security measures; Machine learning; Malware (Computer software); Mobile computing; Smartphones; User interfaces (Computer systems)
Subjects: 000 Computer science, information & general works
Department: School of Computing and Digital Media
Depositing User: Mary Burslem
Date Deposited: 02 Feb 2017 16:36
Last Modified: 02 Feb 2017 16:36
URI: http://repository.londonmet.ac.uk/id/eprint/1199

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