Optimizing Random Forest using Genetic Algorithm for static Android malware detection
Keywords:
Random Forest, Genetic Algorithm, Drebin-215, Malware detection, Static analysisAbstract
The massive and increasingly complex growth of Android malware has led to a heightened threat to mobile device security. One proven effective approach is the use of machine learning, particularly the Random Forest algorithm. However, many previous studies have not fully utilized the potential of this algorithm, as they rely on default configurations without tuning. This study implements the Genetic Algorithm (GA) method to perform hyperparameter tuning on Random Forest, using the Drebin-215 dataset, which consists of 15,036 APKs and 215 static features. The study compares three configurations: Default and GA Tuning. Evaluation is conducted using 5-fold cross-validation and performance metrics including accuracy, precision, recall, and F1-score. The results show that the GA-Tuned model delivers the best performance, achieving an accuracy of 98.97%. The study also tests the model on 55 real APKs to evaluate its predictive capability in real-world cases.
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References
S. Y. Yerima and S. Sezer, “DroidFusion: A novel multilevel classifier fusion approach for Android malware
detection,” IEEE Trans. Cybern., vol. 49, no. 2, pp. 453–466, Feb. 2019. https://doi.org/10.1109/TCYB.2017.2771549
P. Agrawal and B. Trivedi, “Machine learning classifiers for Android malware detection,” in Machine Learning
Approaches for Convergence of IoT and Blockchain, Springer, 2021, pp. 343–352. https://doi.org/10.1007/978- 981-15-5616-6_22
P. Morán, A. Robles-Gómez, A. Duque, L. Tobarra, and R. Pastor-Vargas, “Machine learning model and dimensionality reduction for improving Android malware detection,” PeerJ Comput. Sci., vol. 10, p. e2616, 2024. https://doi.org/10.7717/peerj-cs.2616
M. Cao, Z. Huang, Z. Wang, H. Yang, and Y. Zhang, “On benign features in malware detection,” in Proc. 35th Annu.ACM Symp. Applied Comput., 2020, pp. 920–928. https://doi.org/10.1145/3341105.3374064
D. Arp, M. Spreitzenbarth, M. Hübner, H. Gascon, and K. Rieck, “DREBIN: Effective and explainable detection of Android malware in your pocket,” in Proc. NDSS, 2014. [Online]. Available: https://doi.org/10.14722/ndss.2014.23247
A. Mahindru and P. Singh, “A study of machine learning algorithms for detection of Android malware,” Int. J. Eng. Comput. Sci., vol. 6, no. 6, pp. 21894–21897, 2017.
L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” arXiv preprint, arXiv:2007.15745, 2022. https://arxiv.org/abs/2007.15745
A. S. Wicaksono and A. A. Supianto, “Hyperparameter optimization using genetic algorithm on machine learning methods for online news popularity prediction,” Int. J. Adv. Comput. Sci. Appl. (IJACSA), vol. 9, no. 12, pp. 263–270, 2018.
F. Widardo and A. Chowanda, “Hyperparameter tuning for deep learning model used in multimodal emotion recognition data,” Int. J. Electr. Eng. Informatics (IJEEI), vol. 14, no. 1, pp. 115–123, 2025. https://doi.org/10.11591/eei.v14i1.8707
M. Aftab et al., “Hyper-parameter tuning through innovative designing to avoid over-fitting in machine learning modelling,” J. Stat. Comput. Simul., 2025. https://doi.org/10.1080/00949655.2025.2465787
M. S. Rana, A. Ahmad, B. Nazir, and A. A. Khan, “Evaluation of tree-based machine learning classifiers for Android malware detection,” in Advances in Intelligent Systems and Computing, vol. 701, Springer, 2018, pp. 395–404, 2020 https://doi.org/10.1007/978-3-319-98446-9_35,
M. Albadr, S. Tiun, M. Ayob, and F. T. Al-Dhief, “Genetic algorithm based on natural selection theory for optimization problems,” Symmetry, vol. 12, no. 11, p. 1758, 2020. https://doi.org/10.3390/sym12111758
M. U. Siregar, S. Effendy, and N. Nurhidayati, “Optimized Random Forest classifier based on genetic algorithm for heart failure prediction,” in Proc. Seminar Informatika UIN Sunan Kalijaga, 2023.
P. R. Togatorop, R. N. Purba, and A. Junaidi, “Optimizing Random Forest using Genetic Algorithm for heart disease classification,” LIKMI J. Inf. Technol. Informatics, vol. 13, no. 1, pp. 39–47, 2022. https://doi.org/10.24843/LKJITI.2022.v13.i01.p06
D. Vanusha, J. Kanesan, N. A. Wahid, and B. Shanmugam, “SecuDroid: Android malware detection using ML classifier on static features,” in Proc. 2nd Int. Conf. Netw. Commun. (ICNWC), 2024, pp. 83–88.
A. Jamshidnezhad and M. J. Nordin, “A classifier model based on the features quantitative analysis for facial expression recognition,” in Proc. Int. Conf. on Advanced Science, Engineering and Information Technology (ICASEIT), Bangi-Putrajaya, Malaysia, Jan. 2011, pp. 391–394. https://doi.org/10.18517/ijaseit.1.4.81
R. B. Hadiprakoso, “Analisis statis deteksi malware Android menggunakan algoritma supervised machine learning,” CyberSecurity dan Forensik Digital, vol. 5, no. 1, pp. 1–5, 2022.
S. Dutt, S. Chandramouli, and A. K. Das, “Machine Learning”, 1st ed. Pearson Education, 2018.
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