Machine learning for malware detection
Smartphones have been an integral part of our daily lives today. From instant messaging to performing online banking, smartphones have brought tremendous convenience to the people but also an ever-increasing reliance on them. With Android smartphones having the largest user base in the smartphone ma...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/70351 |
| _version_ | 1826121693453615104 |
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| author | Hong, Qing Fu |
| author2 | Lin Shang-Wei |
| author_facet | Lin Shang-Wei Hong, Qing Fu |
| author_sort | Hong, Qing Fu |
| collection | NTU |
| description | Smartphones have been an integral part of our daily lives today. From instant messaging to performing online banking, smartphones have brought tremendous convenience to the people but also an ever-increasing reliance on them. With Android smartphones having the largest user base in the smartphone market, Android applications have become a means for attackers to infect smartphones with malware in an attempt to gain benefits. Therefore, it is critical to be able to identify malware effectively.
In this project, the focus will be to experiment the viability of system call graphs together with machine learning to construct a malware detector, aiming to classify if an android application is malicious or benign. Different machine learning algorithms will also be experimented and compared to evaluate their results.
The report will conclude with recommendations for future work at the end. |
| first_indexed | 2024-10-01T05:36:40Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/70351 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:36:40Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/703512023-03-03T20:34:41Z Machine learning for malware detection Hong, Qing Fu Lin Shang-Wei School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering Smartphones have been an integral part of our daily lives today. From instant messaging to performing online banking, smartphones have brought tremendous convenience to the people but also an ever-increasing reliance on them. With Android smartphones having the largest user base in the smartphone market, Android applications have become a means for attackers to infect smartphones with malware in an attempt to gain benefits. Therefore, it is critical to be able to identify malware effectively. In this project, the focus will be to experiment the viability of system call graphs together with machine learning to construct a malware detector, aiming to classify if an android application is malicious or benign. Different machine learning algorithms will also be experimented and compared to evaluate their results. The report will conclude with recommendations for future work at the end. Bachelor of Engineering (Computer Science) 2017-04-21T01:12:36Z 2017-04-21T01:12:36Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70351 en Nanyang Technological University 42 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Hong, Qing Fu Machine learning for malware detection |
| title | Machine learning for malware detection |
| title_full | Machine learning for malware detection |
| title_fullStr | Machine learning for malware detection |
| title_full_unstemmed | Machine learning for malware detection |
| title_short | Machine learning for malware detection |
| title_sort | machine learning for malware detection |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/70351 |
| work_keys_str_mv | AT hongqingfu machinelearningformalwaredetection |