Applying Bayesian probability for Android malware detection using permission features
he tremendous rise of mobile technology has boosted malware and has raised the threat of malware. The proliferation of malware has given a great concern among mobile users. Various approaches have been applied to prevent malware spread, including firewalls, antivirus software and many more methods....
| Main Authors: | , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2021
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/32537/ http://umpir.ump.edu.my/id/eprint/32537/1/Applying%20Bayesian%20probability%20for%20Android%20malware.pdf |
| _version_ | 1848824046434648064 |
|---|---|
| author | Sharfah Ratibah, Tuan Mat Mohd Faizal, Ab Razak Mohd Nizam, Mohmad Kahar Juliza, Mohamad Arif Azlee, Zabidi |
| author_facet | Sharfah Ratibah, Tuan Mat Mohd Faizal, Ab Razak Mohd Nizam, Mohmad Kahar Juliza, Mohamad Arif Azlee, Zabidi |
| author_sort | Sharfah Ratibah, Tuan Mat |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | he tremendous rise of mobile technology has boosted malware and has raised the threat of malware. The proliferation of malware has given a great concern among mobile users. Various approaches have been applied to prevent malware spread, including firewalls, antivirus software and many more methods. Google has provided permission features as the main security to filter out the possibility of malware-infected Android mobile. Nevertheless, some permissions immediately granted by Android without user confirmation. This paper proposes a malware detection system based on permission features using Bayesian probability to battle the malware issue. This study used 96,074 samples retrieved from Androzoo and Drebin. By using static analysis, this study focuses on permission features that are significant in Android applications. The experiments conducted using chi-square as an algorithm and Naïve Bayes as a classifier. The accuracy of the detection is 85%. In conclusion, the detection of Android malware using the dataset has produced a good performance. |
| first_indexed | 2025-11-15T03:06:48Z |
| format | Conference or Workshop Item |
| id | ump-32537 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:06:48Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-325372021-11-05T05:13:54Z http://umpir.ump.edu.my/id/eprint/32537/ Applying Bayesian probability for Android malware detection using permission features Sharfah Ratibah, Tuan Mat Mohd Faizal, Ab Razak Mohd Nizam, Mohmad Kahar Juliza, Mohamad Arif Azlee, Zabidi QA75 Electronic computers. Computer science he tremendous rise of mobile technology has boosted malware and has raised the threat of malware. The proliferation of malware has given a great concern among mobile users. Various approaches have been applied to prevent malware spread, including firewalls, antivirus software and many more methods. Google has provided permission features as the main security to filter out the possibility of malware-infected Android mobile. Nevertheless, some permissions immediately granted by Android without user confirmation. This paper proposes a malware detection system based on permission features using Bayesian probability to battle the malware issue. This study used 96,074 samples retrieved from Androzoo and Drebin. By using static analysis, this study focuses on permission features that are significant in Android applications. The experiments conducted using chi-square as an algorithm and Naïve Bayes as a classifier. The accuracy of the detection is 85%. In conclusion, the detection of Android malware using the dataset has produced a good performance. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/32537/1/Applying%20Bayesian%20probability%20for%20Android%20malware.pdf Sharfah Ratibah, Tuan Mat and Mohd Faizal, Ab Razak and Mohd Nizam, Mohmad Kahar and Juliza, Mohamad Arif and Azlee, Zabidi (2021) Applying Bayesian probability for Android malware detection using permission features. In: IEEE 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM) , 24-26 August 2021 , Pekan, Pahang, Malaysia. pp. 574-579.. ISBN 978-1-6654-1407-4 (Published) https://doi.org/10.1109/ICSECS52883.2021.00111 |
| spellingShingle | QA75 Electronic computers. Computer science Sharfah Ratibah, Tuan Mat Mohd Faizal, Ab Razak Mohd Nizam, Mohmad Kahar Juliza, Mohamad Arif Azlee, Zabidi Applying Bayesian probability for Android malware detection using permission features |
| title | Applying Bayesian probability for Android malware detection using permission features |
| title_full | Applying Bayesian probability for Android malware detection using permission features |
| title_fullStr | Applying Bayesian probability for Android malware detection using permission features |
| title_full_unstemmed | Applying Bayesian probability for Android malware detection using permission features |
| title_short | Applying Bayesian probability for Android malware detection using permission features |
| title_sort | applying bayesian probability for android malware detection using permission features |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/32537/ http://umpir.ump.edu.my/id/eprint/32537/ http://umpir.ump.edu.my/id/eprint/32537/1/Applying%20Bayesian%20probability%20for%20Android%20malware.pdf |