ABDULLAH, NORLIZA BINTI (2019) Android Malware Detection System using Genetic Programming. PhD thesis, University of York.
Abstract
Nowadays, smartphones and other mobile devices are playing a significant role in the way people engage in entertainment, communicate, network, work, and bank and shop online. As the number of mobile phones sold has increased dramatically worldwide, so have the security risks faced by the users, to a degree most do not realise. One of the risks is the threat from mobile malware. In this research, we investigate how supervised learning with evolutionary computation can be used to synthesise a system to detect Android mobile phone attacks. The attacks include malware, ransomware and mobile botnets. The datasets used in this research are publicly downloadable, available for use with appropriate acknowledgement. The primary source is Drebin. We also used ransomware and mobile botnet datasets from other Android mobile phone researchers. The research in this thesis uses Genetic Programming (GP) to evolve programs to distinguish malicious and non-malicious applications in Android mobile datasets. It also demonstrates the use of GP and Multi-Objective Evolutionary Algorithms (MOEAs) together to explore functional (detection rate) and non-functional (execution time and power consumption) trade-offs. Our results show that malicious and non-malicious applications can be distinguished effectively using only the permissions held by applications recorded in the application's Android Package (APK). Such a minimalist source of features can serve as the basis for highly efficient Android malware detection. Non-functional tradeoffs are also highlight.
Metadata
Keywords: | Supervised Learning; Genetic Programing; Multi-objective Genetic Algorithm; SPEA2; Android Malware |
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Awarding institution: | University of York |
Academic Units: | The University of York > Computer Science (York) |
Identification Number/EthosID (e.g. uk.bl.ethos.123456): | uk.bl.ethos.832567 |
Depositing User: | Ms Norliza Abdullah |
Date Deposited: | 28 Jun 2021 10:52 |
Last Modified: | 21 Jul 2021 09:53 |
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Filename: Abdullah_201051902.pdf
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