ავტორი: Bilal Ahmad Kamal, Adeela Muhammad Askari, Salman Tariq
ორგანიზაცია: Air University Islamabad Pakistan


საკვანძო სიტყვები: support vector machine, ML, Android Apps, malicious
აბსტრაქტი. The use of smartphones has wisely evolved in the 20th century. Many people all over the world can connect to their smartphones in a variety of ways. Some invaders are leveraging the power of the rapidly growing smartphone usage to Developing rogue Android applications to steal handsets' sensitive data To address these grave issues, a malicious program that is both effective and efficient is required. Numerous malware detection programs have historically been built, but some of them are not able to identify recently developed malware programs or programs contaminated with different Trojan horses, worms, and spyware. The software for detecting fraudulent programs can be enhanced especially thanks to ML algorithms. The system uses ML classification algorithms like Support Vector Machine (SVM) to improve the malware application detection in the proposed system. The proposed ML classification and fusion algorithms will improve performance metrics like the accuracy of malware application exposure and decrease the complexity of the detection process. The suggested approach integrates detecting software with a training application that users can install on Android cellphones to flag hazardous activities when they are accessible.


1.Vinod, P., Zemmari, A., & Conti, M. (2019). A machine learning based approach to detect malicious Android apps using discriminant system calls. Future Generation Computer Systems, 94, 333-350.
2.Xiao, J. X., Lu, Z. C., & Xu, Q. H. (2018, December). A new Android malicious application detection method using feature importance score. In Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence (pp. 145-150).
3.Kambar, M. E. Z. N., Esmaeilzadeh, A., Kim, Y., & Taghva, K. (2022, January). A survey on mobile malware detection methods using machine learning. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0215-0221). IEEE.
4.Arslan, R. S. (2021). AndroAnalyzer: Android malicious software detection based on deep learning. PeerJ Computer Science, 7, e533.
5.Jiang, X., Mao, B., Guan, J., & Huang, X. (2020). Android malware detection using fine-grained features. Scientific Programming, 2020. la Puerta, J. G., Pastor-López, I., Porto, I., Sanz, B., & Bringas, P. G. (2021). Detecting malicious Android applications based on the network packets generated. Neurocomputing, 456, 629-636.
7.Mohamed, S. E., Ashaf, M., Ehab, A., Shereef, O., Metwaie, H., & Amer, E. (2021, May). Detecting Malicious Android Applications Based On API calls and Permissions Using Machine learning Algorithms. In 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) (pp. 1-6). IEEE.
8.Jung, J., Lim, K., Kim, B., Cho, S. J., Han, S., & Suh, K. (2019, June). Detecting malicious Android apps using the popularity and relations of APIs. In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 309-312). IEEE.
9.Razgallah, A., Khoury, R., Hallé, S., & Khanmohammadi, K. (2021). A survey of malware detection in Android apps: Recommendations and perspectives for future research. Computer Science Review, 39, 100358.
10.OS, J. N. (2021). Detection of malicious Android applications using Ontology-based intelligent model in mobile cloud environment. Journal of Information Security and Applications, 58, 102751.
11.Liu, L., Ren, W., Xie, F., Yi, S., Yi, J., & Jia, P. (2021). Learning-Based Detection for Malicious Android Application Using Code Vectorization. Security and Communication Networks, 2021.
12.Sharma, T., & Rattan, D. (2021). Malicious application detection in Android—a systematic literature review. Computer Science Review, 40, 100373.
13.Song, Y., Geng, Y., Wang, J., Gao, S., & Shi, W. (2021). Permission Sensitivity-Based Malicious Application Detection for Android. Security and Communication Networks, 2021.
14.Chen, X. R., Shi, S. S., Xie, C. L., Yang, Z., Guo, Y. J., Fang, Y., & Wen, W. P. (2021, February). SUIP: An Android malware detection method based on data flow features. In Journal of Physics: Conference Series (Vol. 1812, No. 1, p. 012010). IOP Publishing.