FS3E: a Tool for Executing and Evaluating Feature Selection Methods for Android Malware Detection

• Estevão Costa UFAM
• Diego Kreutz UNIPAMPA https://orcid.org/0000-0003-0830-0238
• Vanderson Rocha UFAM https://orcid.org/0000-0003-3103-7749
• Luíza Leão UFAM https://orcid.org/0000-0003-1962-2873
• Sávio Sabóia UFAM http://orcid.org/0000-0002-0036-8425
• Nicolas Neves UFAM
• Eduardo Feitosa UFAM https://orcid.org/0000-0001-6401-3992

DOI: https://doi.org/10.5753/sbseg_estendido.2022.227041

Abstract

Currently, there are several feature selection methods. However, it is difficult to find a working implementation for assessing the quality of the methods on sets of datasets. As a first solution to this problem, we implemented FS3E, a software framework for making available implementations and automating the evaluation of feature selection methods. In the first version of FS3E, we implemented, made available, and evaluated five selection methods designed for Android malware detection.

Keywords: Feature Selection, Methods, Android Malware, Tool

References

Anggraeni, A., Mustofa, K., and Priyanta, S. (2021). Comparison of filter and wrapper based feature selection methods on spam comment classification. IJCCS, 15(3):245–254.

Cai, L., Li, Y., and Xiong, Z. (2021). JOWMDroid: Android malware detection based on feature weighting with joint optimization of weight-mapping and classifier parameters. Computers & Security, 100:102086.

Damodaran, A., Di Troia, F., Visaggio, C. A., Austin, T., and Stamp, M. (2017). A comparison of static, dynamic, and hybrid analysis for malware detection. Journal of Computer Virology and Hacking Techniques, 13.

Feizollah, A., Anuar, N. B., Salleh, R., and Wahab, A. W. A. (2015). A review on feature selection in mobile malware detection. Digital Investigation, 13:22–37.

Galib, A. H. and Hossain, B. M. M. (2020). Significant API calls in android malware detection (using feature selection techniques and correlation based feature elimination). In García-Castro, R., editor, The 32nd SEKE, pages 566–571. KSI Research Inc.

Golrang, A., Yayilgan, S. Y., and Elezaj, O. (2021). The multi-objective feature selection in android malware detection system. In Yildirim Yayilgan, S., Bajwa, I. S., and Sanfilippo, F., editors, Intelligent Technologies and Applications, pages 311–322, Cham.

Lee, J., Jang, H., Ha, S., and Yoon, Y. (2021). Android malware detection using machine learning with feature selection based on the genetic algorithm. Mathematics, 9(21).

Mahindru, A. and Sangal, A. L. (2019). DeepDroid: Feature selection approach to detect android malware using deep learning. In ICSESS, pages 16–19.

Mahindru, A. and Sangal, A. L. (2021). Semidroid: a behavioral malware detector based on unsupervised machine learning techniques using feature selection approaches. International Journal of Machine Learning and Cybernetics, 12:1411.

Moutaz, A. (2020). Automated malware detection in mobile app stores based on robust feature generation. Electronics, 9:435.

Nivaashini, M., Soundariya, R. S., Vidhya Shri, H., and Thangaraj, P. (2018). Comparative analysis of feature selection methods and machine learning algorithms in permission based android malware detection. In I2C2SW, pages 72–77.

Qiu, J., Zhang, J., Luo, W., Pan, L., Nepal, S., and Xiang, Y. (2020). A survey of android malware detection with deep neural models. ACM Comput. Surv., 53(6).

Remeseiro, B. and Bolon-Canedo, V. (2019). A review of feature selection methods in medical applications. Computers in Biology and Medicine, 112:103375.

Roy, S., DeLoach, J., Li, Y., Herndon, N., Caragea, D., Ou, X., Ranganath, V. P., Li, H., and Guevara, N. (2015). Experimental study with real-world data for android app security analysis using machine learning. In 31st ACSAC, page 81–90, New York, NY, USA. Association for Computing Machinery.

Sharma, T. and Rattan, D. (2021). Malicious application detection in android—a systematic literature review. Computer Science Review, 40:100373.

Smmarwar, S. K., Gupta, G. P., and Kumar, S. (2022). A hybrid feature selection approach-based android malware detection framework using machine learning techniques. In Agrawal, D. P., Nedjah, N., Gupta, B. B., and Martinez Perez, G., editors, Cyber Security, Privacy and Networking, pages 347–356, Singapore. Springer Nature Singapore.

Soares, T., Kreutz, D., Rocha, V., Costa, E., Leao, L., Pontes, J., Assolin, J., Rodrigues, G., and Feitosa, E. (2022). Uma análise de métodos de seleção de características aplicados `a detecção de malwares android. In SBSeg22.

Sun, L., Li, Z., Yan, Q., Srisa-an, W., and Pan, Y. (2016). SigPID: significant permission identification for android malware detection. In MALWARE, pages 1–8.

Venkatesh, B. and Anuradha, J. (2019). A review of feature selection and its methods. Cybernetics and Information Technologies, 19(1):3–26.

Wu, Y., Li, M., Wang, J., Fang, Z., Zeng, Q., Yang, T., and Cheng, L. (2022). Droidrl: Reinforcement learning driven feature selection for android malware detection. https://arxiv.org/abs/2203.02719.

Xiao, J., Xu, K., and Duan, J. (2019). Malicious android application detection based on composite features. In Proceedings of the 3rd International Conference on Computer Science and Application Engineering, CSAE 2019, New York, NY, USA. Association for Computing Machinery.

Sahin, D., Kural, O., Akleylek, S., and Kilic, E. (2021). A novel permission-based android malware detection system using feature selection based on linear regression. Neural Computing and Applications, pages 1–16.