Abstract
As the dominant operating system for mobile devices, Android is the prime target of malicious attackers. Installed Android applications provide an opportunity for attackers to bypass the system’s security. Therefore, it is vital to study and evaluate Android applications to effectively identify harmful applications. Android applications are analyzed by conventional methods using signature hash-based algorithms or static features-based machine learning approaches. This research proposes optimized ensemble classification models for Android applications. Ensemble models have been trained for both static and dynamic analysis using seven and eight distinct classifiers respectively. These models have been optimized by tuning their hyper-parameters and evaluated using K-fold cross-validation. We were able to acquire an F1 score of 99.27% and an accuracy of 99.47% for static analysis and our dynamic analysis model yielded an F1 score of 96.96% and an accuracy of 96.66%. Our proposed approach overcomes conventional solutions by taking into account both static and dynamic analysis and attaining high accuracy with the help of ensemble models.
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References
Kural, O., Şahin, D., Akleylek, S., Kılıç, E., Ömüral, M.: Apk2Img4AndMal: Android Malware Detection Framework Based on Convolutional Neural Network. In: 2021 6th International Conference on Computer Science and Engineering (UBMK), pp. 731–734 (2021)
Mat, S., Razak, M., Kahar, M., Arif, J., Firdaus, A.: A Bayesian probability model for Android malware detection. ICT Express. (2021), https://www.sciencedirect.com/science/article/pii/S2405959521001235
Abdullah, T., Ali, W., Abdulghafor, R.: Empirical study on intelligent android malware detection based on supervised machine learning. Int. J. Adv. Comput. Sci. Appl. 11 (2020), https://dx.doi.org/10.14569/IJACSA.2020.0110429
Türker, S., Can, A.: AndMFC: Android Malware Family Classification Framework. In: 2019 IEEE 30th International Symposium on Personal, Indoor And Mobile Radio Communications (PIMRC Workshops). 1, 1–6 (2019)
Park, J., Chun, H., Jung, S.: API and permission-based classification system for Android malware analysis. In: 2018 International Conference on Information Networking (ICOIN), pp. 930–935 (2018)
Fiky, A., Elshenawy, A., Madkour, M. Detection of android malware using machine learning. In: 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), pp. 9–16 (2021)
Yerima, S.: Android malware dataset for machine learning 2. (figshare 2018,2), https://tinyurl.com/drebinData
Razgallah, A.: Android Apps system calls traces. (kaggle 2021,3), https://www.kaggle.com/razgallah/android-apps-system-calls-traces
Hu, C., Jeng, T., Chen, Y.: Dynamic Android Malware Analysis with De-Identification of Personal Identifiable Information. (Association for Computing Machinery, 2020), https://doi.org/10.1145/3418688.3418694
Islam, F., Jamil, A., Momen, S.: Evaluation of Machine Learning Methods for Android Malware Detection using Static Features. In: 2021 IEEE International Conference On Artificial Intelligence in Engineering and Technology (IICAIET), pp. 1–6 (2021)
Fatima, A., Maurya, R., Dutta, M., Burget, R., Masek, J.: Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning. In: 2019 42nd International Conference On Telecommunications and Signal Processing (TSP), pp. 220–223 (2019)
Palikhe, A., Li, L., Tian, F., Kar, D., Zhang, N., Zhang, W.: MalDuoNet: A DualNet Framework to Detect Android Malware. In: 2021 RIVF International Conference on Computing and Communication Technologies (RIVF), pp. 1–6 (2021)
Amer, E.: Permission-based approach for android malware analysis through ensemble-based voting model. In: 2021 International Mobile, Intelligent, And Ubiquitous Computing Conference (MIUCC), pp. 135–139 (2021)
Smmarwar, S., Gupta, G., Kumar, S., Kumar, P.: An optimized and efficient android malware detection framework for future sustainable computing. Sustain. Energy Technol. Assessments. 54, 102852 (2022). https://www.sciencedirect.com/science/article/pii/S2213138822009006
Jannat, U., Hasnayeen, S., Bashar Shuhan, M., Ferdous, M.: Analysis and detection of malware in android applications using machine learning. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–7 (2019)
Thangaveloo, R., Jing, W., Chiew, K., Abdullah, J.: DATDroid: Dynamic analysis technique in android malware detection. Int. J. Adv. Sci. Eng. Inform. Technol. 10, 536 (2020,3)
Roopak, S., Thomas, T., Emmanuel, S.: Android malware detection mechanism based on bayesian model averaging. Recent Findings In Intelligent Computing Techniques, pp. 87–96 (2019)
Han, H., Lim, S., Suh, K., Park, S., Cho, S., Park, M.: Enhanced android malware detection: an svm-based machine learning approach. In: 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 75–81 (2020)
Islam, F., Jamil, A., Momen, S.: Evaluation of machine learning methods for android malware detection using static features. In: 2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), pp. 1–6 (2021)
Mohamed, S., Ashaf, M., Ehab, A., Abdalla, O., Metwaie, H., Amer, E.: Detecting Malicious Android Applications Based on API calls and Permissions Using Machine learning Algorithms (2021)
Arslan, R.: Identify Type of Android Malware with Machine Learning Based Ensemble Model. In: 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 628–632 (2021)
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Jain, S., Agrawal, A., Nayak, S.S., Kakelli, A.K. (2023). Optimized Static and Dynamic Android Malware Analysis Using Ensemble Learning. In: Sharma, H., Saha, A.K., Prasad, M. (eds) Proceedings of International Conference on Intelligent Vision and Computing (ICIVC 2022). ICIVC 2022. Proceedings in Adaptation, Learning and Optimization, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-031-31164-2_14
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