CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning
Today many smart devices are running on Android systems. With the increasing popularity of Android, mobile malware continuously evolves as well, and further attacks Android operating systems. To address these shortcoming issues many security experts use different approaches to detect malware based o...
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40367/ http://umpir.ump.edu.my/id/eprint/40367/1/CAGDEEP_Mobile%20malware%20analysis%20using%20force%20atlas%202.pdf http://umpir.ump.edu.my/id/eprint/40367/2/CAGDEEP_Mobile%20malware%20analysis%20using%20force%20atlas%202%20with%20strong%20gravity%20call%20graph%20and%20deep%20learning_ABS.pdf |
| _version_ | 1848826028885016576 |
|---|---|
| author | Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Azlee, Zabidi Mohd Faizal, Ab Razak |
| author_facet | Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Azlee, Zabidi Mohd Faizal, Ab Razak |
| author_sort | Nur Khairani, Kamarudin |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Today many smart devices are running on Android systems. With the increasing popularity of Android, mobile malware continuously evolves as well, and further attacks Android operating systems. To address these shortcoming issues many security experts use different approaches to detect malware based on various static features. However, by considering only the statistical features, the potential semantic information such as the behavioral feature of the code is overlooked. To leverage the existing static analysis techniques, this study proposes CAGDeep, to reflect deep semantic information of malware samples. The novelty of our study lies in the Force Atlas 2 call graph development to capture malware behavior patterns. Afterwards, this study adopts Convolutional Neural Network (CNN) for malware detection and classification algorithm. We compare CAGDeep with a state-of-the-art Android malware detection approach. Our evaluation results demonstrate that CAGDeep can achieve 80% accuracy for detecting malware. |
| first_indexed | 2025-11-15T03:38:19Z |
| format | Conference or Workshop Item |
| id | ump-40367 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:38:19Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-403672024-04-16T04:16:52Z http://umpir.ump.edu.my/id/eprint/40367/ CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Azlee, Zabidi Mohd Faizal, Ab Razak QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Today many smart devices are running on Android systems. With the increasing popularity of Android, mobile malware continuously evolves as well, and further attacks Android operating systems. To address these shortcoming issues many security experts use different approaches to detect malware based on various static features. However, by considering only the statistical features, the potential semantic information such as the behavioral feature of the code is overlooked. To leverage the existing static analysis techniques, this study proposes CAGDeep, to reflect deep semantic information of malware samples. The novelty of our study lies in the Force Atlas 2 call graph development to capture malware behavior patterns. Afterwards, this study adopts Convolutional Neural Network (CNN) for malware detection and classification algorithm. We compare CAGDeep with a state-of-the-art Android malware detection approach. Our evaluation results demonstrate that CAGDeep can achieve 80% accuracy for detecting malware. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40367/1/CAGDEEP_Mobile%20malware%20analysis%20using%20force%20atlas%202.pdf pdf en http://umpir.ump.edu.my/id/eprint/40367/2/CAGDEEP_Mobile%20malware%20analysis%20using%20force%20atlas%202%20with%20strong%20gravity%20call%20graph%20and%20deep%20learning_ABS.pdf Nur Khairani, Kamarudin and Ahmad Firdaus, Zainal Abidin and Azlee, Zabidi and Mohd Faizal, Ab Razak (2023) CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 396-401. (192961). ISBN 979-835031093-1 (Published) https://doi.org/10.1109/ICSECS58457.2023.10256350 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Nur Khairani, Kamarudin Ahmad Firdaus, Zainal Abidin Azlee, Zabidi Mohd Faizal, Ab Razak CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning |
| title | CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning |
| title_full | CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning |
| title_fullStr | CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning |
| title_full_unstemmed | CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning |
| title_short | CAGDEEP : Mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning |
| title_sort | cagdeep : mobile malware analysis using force atlas 2 with strong gravity call graph and deep learning |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/40367/ http://umpir.ump.edu.my/id/eprint/40367/ http://umpir.ump.edu.my/id/eprint/40367/1/CAGDEEP_Mobile%20malware%20analysis%20using%20force%20atlas%202.pdf http://umpir.ump.edu.my/id/eprint/40367/2/CAGDEEP_Mobile%20malware%20analysis%20using%20force%20atlas%202%20with%20strong%20gravity%20call%20graph%20and%20deep%20learning_ABS.pdf |