Behavioural-based malware detection on android phones
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 fr...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/148616 |
| _version_ | 1811684286351278080 |
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| author | Kyran Ming Kuttan |
| author2 | Liu Yang |
| author_facet | Liu Yang Kyran Ming Kuttan |
| author_sort | Kyran Ming Kuttan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:26:13Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/148616 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:26:13Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1486162021-05-07T13:23:09Z Behavioural-based malware detection on android phones Kyran Ming Kuttan Liu Yang School of Computer Science and Engineering yangliu@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Pattern recognition 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. Bachelor of Engineering (Computer Engineering) 2021-05-07T13:21:00Z 2021-05-07T13:21:00Z 2021 Final Year Project (FYP) 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 https://hdl.handle.net/10356/148616 en SCSE20-197 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Kyran Ming Kuttan Behavioural-based malware detection on android phones |
| title | Behavioural-based malware detection on android phones |
| title_full | Behavioural-based malware detection on android phones |
| title_fullStr | Behavioural-based malware detection on android phones |
| title_full_unstemmed | Behavioural-based malware detection on android phones |
| title_short | Behavioural-based malware detection on android phones |
| title_sort | behavioural based malware detection on android phones |
| topic | Engineering::Computer science and engineering::Computing methodologies::Pattern recognition |
| url | https://hdl.handle.net/10356/148616 |
| work_keys_str_mv | AT kyranmingkuttan behaviouralbasedmalwaredetectiononandroidphones |