Deep learning of large-scale android malware detection
Smartphones had brought convenience to our lives. However, malware attacks can easily disrupt this convenience. Given the large Android market size and its vulnerability to malware, this report will focus on Android malware detection by the means of machine learning and deep learning. In this re...
| Main Author: | |
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/74923 |
| _version_ | 1811687031979376640 |
|---|---|
| author | Loo, Jia Yi |
| author2 | Chen Lihui |
| author_facet | Chen Lihui Loo, Jia Yi |
| author_sort | Loo, Jia Yi |
| collection | NTU |
| description | Smartphones had brought convenience to our lives. However, malware attacks can
easily disrupt this convenience. Given the large Android market size and its
vulnerability to malware, this report will focus on Android malware detection by the
means of machine learning and deep learning.
In this report, a re-implementation of a newly proposed machine learning method
was done and tested on large real world datasets. Extensive simulations had been
conducted with various parameters. The simulations included classification of
applications into benign and malware, using both machine learning and deep learning
methods, the clustering of malware families and clone applications.
It was found that classification using machine learning was more efficient and
accurate than that of deep learning. As for the two clustering applications, after a
series of experiment, it was concluded that Agglomerative clustering model with
ward linkages was the best model to be used.
The findings obtained would give a more detailed understanding of the behaviour of
malware applications as well as the types of methods suitable for Android malware
detection. Due to some limitations, it is recommended that more simulations to be
done so as to give even more detailed findings. |
| first_indexed | 2024-10-01T05:09:51Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/74923 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:09:51Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/749232023-07-07T15:55:11Z Deep learning of large-scale android malware detection Loo, Jia Yi Chen Lihui School of Electrical and Electronic Engineering DRNTU::Engineering Smartphones had brought convenience to our lives. However, malware attacks can easily disrupt this convenience. Given the large Android market size and its vulnerability to malware, this report will focus on Android malware detection by the means of machine learning and deep learning. In this report, a re-implementation of a newly proposed machine learning method was done and tested on large real world datasets. Extensive simulations had been conducted with various parameters. The simulations included classification of applications into benign and malware, using both machine learning and deep learning methods, the clustering of malware families and clone applications. It was found that classification using machine learning was more efficient and accurate than that of deep learning. As for the two clustering applications, after a series of experiment, it was concluded that Agglomerative clustering model with ward linkages was the best model to be used. The findings obtained would give a more detailed understanding of the behaviour of malware applications as well as the types of methods suitable for Android malware detection. Due to some limitations, it is recommended that more simulations to be done so as to give even more detailed findings. Bachelor of Engineering 2018-05-25T01:23:26Z 2018-05-25T01:23:26Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74923 en Nanyang Technological University 60 p. application/pdf |
| spellingShingle | DRNTU::Engineering Loo, Jia Yi Deep learning of large-scale android malware detection |
| title | Deep learning of large-scale android malware detection |
| title_full | Deep learning of large-scale android malware detection |
| title_fullStr | Deep learning of large-scale android malware detection |
| title_full_unstemmed | Deep learning of large-scale android malware detection |
| title_short | Deep learning of large-scale android malware detection |
| title_sort | deep learning of large scale android malware detection |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/74923 |
| work_keys_str_mv | AT loojiayi deeplearningoflargescaleandroidmalwaredetection |