Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148616
Title: Behavioural-based malware detection on android phones
Authors: Kyran Ming Kuttan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Kyran Ming Kuttan (2021). Behavioural-based malware detection on android phones. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148616
Project: SCSE20-197
Abstract: The Android operating system is one of the most popular mobile operating systems in the market today. Applications developed using said operating system are continuously evolving and that include ones that have malicious intentions. There are many security measures put in place to prevent malware from being released into the application market, for instance permissions and Google Play Shield. However, malware continues to break through such methods as the development of malware continues to improve. In reaction, new methods of detecting malware have been researched to increase the effectiveness of malware detection. In this project, a methodology is proposed where the permissions used by an application is represented in the form of a graph, where the behaviour of an application can be seen. This form of graph can be termed as a permissions graph. An analysis is then conducted through the use of deep learning modes such as Feed-Forward Neural Network models and Neural Structured Learning (NSL) models. By using a permissions graph and an NSL model, the accuracy of detecting malware was desirable but can be improved on.
URI: https://hdl.handle.net/10356/148616
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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