Android Malware Detection using Deep Learning Classification Approach
DOI:
https://doi.org/10.21834/e-bpj.v10iSI40.7714Keywords:
Android Malware, Malware Detection, Deep LearningAbstract
Android devices are becoming increasingly popular, and there are more threats to Android users. This paper discusses Android malware detection using a deep learning classification approach. In this study, Android software was analysed using malware analysis tools, the selected features were extracted, and the results were compiled into a CSV file. Then, its use in CNN and RNN models for malware detection was analysed by measuring accuracy using the standard accuracy formula. According to the development process, CNN performs better at detecting Android malware, achieving 96 per cent accuracy, while RNN achieves 75 per cent accuracy.
References
Arora, A., Peddoju, S.K., Chouhan, V., & Chaudhary, A. (2019). Hybrid Android malware detection by combining supervised and unsupervised learning. 24th Annual International Conference on Mobile Computing and Networking (MobiCom '18). Association for Computing Machinery. pp. 798–800. https://doi.org/10.1145/3241539.3267768. DOI: https://doi.org/10.1145/3241539.3267768
Assegie, T.A. (2021). An Optimised KNN Model for Signature-Based Malware Detection. International Journal of Computer Engineering in Research Trends (IJCERT). Vol. 8, Issue 02, pp. 46-49, https://ijcert.org/ems/ijcert_papers/V8I206.pdf.
Baldini, G., and Geneiatakis, D. (2019). A Performance Evaluation on Distance Measures in KNN for Mobile Malware Detection. 6th International Conference on Control, Decision, and Information Technologies (CoDIT). pp. 193-198. https://doi.org/10.1109/CoDIT.2019.8820510. DOI: https://doi.org/10.1109/CoDIT.2019.8820510
Castillo-Zúñiga, I.; Luna-Rosas, F.; Rodríguez-Martínez, L.; Muñoz-Arteaga, J.; López-Veyna, J.; Rodríguez-Díaz, M.A. (2020). Internet Data Analysis Methodology for Cyberterrorism Vocabulary Detection, Combining Techniques of Big Data Analytics, NLP, and Semantic Web. International Journal on Semantic Web and Information Systems (IJSWIS). 16(1). 69–86. DOI: 10.4018/IJSWIS.2020010104. DOI: https://doi.org/10.4018/IJSWIS.2020010104
Chia, C., Choo, K.-K.R. & Fehrenbacher, D. (2017). How Cyber-Savvy are Older Mobile Device Users? Mobile Security and Privacy. Advances, Challenges, and Future Research Directions. Pages 67-83. https://doi.org/10.1016/B978-0-12-804629-6.00004-3. DOI: https://doi.org/10.1016/B978-0-12-804629-6.00004-3
Droos, A., Al-Mahadeen, A., Al-Harasis, T., Al-Attar, R. & Ababneh, M. (2022). Android Malware Detection Using Machine Learning. 13th International Conference on Information and Communication Systems (ICICS). pp. 36–41. DOI: https://doi.org/10.1109/ICICS55353.2022.9811130
Fedler, R., Kulicke, M., & Schutte, J. (2013). An Antivirus API for Android Malware Recognition. 2013 8th International Conference on Malicious and Unwanted Software:”The Americas” (MALWARE). 77–84. https://doi.org/10.1109/MALWARE.2013.6703688. DOI: https://doi.org/10.1109/MALWARE.2013.6703688
Ghosh, A., & Senthilrajan, A. (2019). Research on Packet Inspection Techniques. International Journal of Scientific & Technology Research. 8. 2068-2073.
Hyoil, H., Lim, S., Suh, K., Park, S., Cho, S. & Park, M. (2020). Enhanced Android Malware Detection: An SVM-Based Machine Learning Approach. IEEE International Conference on Big Data and Smart Computing (BigComp). 75-81. http://dx.doi.org/10.1109/BigComp48618.2020.00-96. DOI: https://doi.org/10.1109/BigComp48618.2020.00-96
Jay, A. (2023). Number of Smartphone and Mobile Phone Users Worldwide in 2020/2021: Demographics, Statistics, Predictions. FinancesOnline. https://financesonline.com/number-of-smartphone-users-worldwide. Accessed: 2023-Jan-11.
Mat, S.R.T., Ab Razak, M.F., Kahar, M.N.M., Arif, J.M., Mohamad, S. & Firdaus, A. (2021). Towards a Systematic Description of the Field Using Bibliometric Analysis:Malware Evolution. Scientometrics, 126, 2013–2055 https://doi.org/10.1007/s11192-020-03834-6. DOI: https://doi.org/10.1007/s11192-020-03834-6
Mbaziira, A.V., Diaz-Gonzales, J. & Liu, M. (2020). Deep Learning in Detection of Mobile Malware. Journal of Computing Sciences in Colleges, vol. 36, no. 3, pp. 80–88.
McLaughlin, N., Martinez Del Rincon, J., Kang, B., Yerima, S., Miller, P., Sezer, S., Safaei, Y., Trickel, E., Zhao, Z., Doupé, A., & Joon Ahn, G. (2017). Deep Android Malware Detection. Proceedings of the Seventh ACM Conference on Data and Application Security and Privacy. https://doi.org/10.1145/3029806.3029823. DOI: https://doi.org/10.1145/3029806.3029823
Nicheporuk, A., Savenko, O., Nicheporuk, A., & Nicheporuk, Y. (2020). An Android Malware Detection Method Based on CNN Mixed-data Model. EasyChair Preprint no. 4345. https://easychair.org/publications/preprint/cpxr.
Qiu, J., Zhang, J., Luo, W., Pan, L., Nepal, S., & Xiang, Y. (2021). A Survey of Android Malware Detection with Deep Neural Models. ACM Computing Surveys, 53(6), 1–36. https://doi.org/10.1145/3417978. DOI: https://doi.org/10.1145/3417978
Shiqi, L. (2019). Android Malware Analysis and Detection Based on Attention-CNN-LSTM. Journal of Computers, 31–43. https://doi.org/10.17706/jcp.14.1.31-43. DOI: https://doi.org/10.17706/jcp.14.1.31-43
Singh, P., Borgohain, S. K. & Kumar, J. (2022). Performance Enhancement of SVM-based ML Malware Detection Model Using Data Preprocessing. 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET). pp. 1–4, doi: 10.1109/ICEFEET51821.2022.9848192. DOI: https://doi.org/10.1109/ICEFEET51821.2022.9848192
Statcounter Global Stats. (2023). Mobile Operating System Market Share Worldwide 2020. Statcounter Global Stats. https://gs.statcounter.com/os-market-share/mobile/worldwide. Accessed: 2023-Jan-11.
Tayyab, U., Khan, F. B., Durad, M. H., Khan, A., & Lee, Y. S. (2022). A Survey of the Recent Trends in Deep Learning Based Malware Detection. Journal of Cybersecurity and Privacy. 2. 800–829. 10.3390/jcp2040041. DOI: https://doi.org/10.3390/jcp2040041
Viennot, N., Garcia, E. & Nieh, J. (2014). A Measurement Study of Google Play. 2014 ACM International Conference on Measurement and Modelling of Computer Systems (SIGMETRICS '14). 221–233. https://doi.org/10.1145/2591971.2592003. DOI: https://doi.org/10.1145/2591971.2592003
Wang, Z., Li, G., Chi, Y., Zhang, J., Yang, T., & Liu, Q. (2019). Android Malware Detection Based on Convolutional Neural Networks. 3rd International Conference on Computer Science and Application Engineering - CSAE 2019. https://doi.org/10.1145/3331453.3361306. DOI: https://doi.org/10.1145/3331453.3361306
Wu, S. (2017). Detecting Remote Access Trojans through External Control at Area Network Borders. ACM/IEEE Symposium on Architectures for Networking and Communications. 10.1109/ANCS.2017.27. DOI: https://doi.org/10.1109/ANCS.2017.27
Yildiz, O. & Dogru, I.A. (2019). Permission-based Android Malware Detection System using Feature Selection with Genetic Algorithm. International Journal of Software Engineering and Knowledge Engineering, Vol. 29, No. 02, pp. 245-262. https://doi.org/10.1142/S0218194019500116. DOI: https://doi.org/10.1142/S0218194019500116
Yilmaz, A. B., Taspinar, Y. S., & Koklu, M. (2022). Classification of Malicious Android Applications Using Naive Bayes and Support Vector Machine Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 269–274. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/2010.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Mohd Faris Mohd Fuzi, Nur Amirah Amri, Mohammad Hafiz Ismail, Mohamad Yusof Darus, Tajul Rosli Razak, Nurul Huda Nik Zulkipli
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
