Uma Análise de Métodos de Seleção de Características aplicados à Detecção de Malwares Android
Resumo
A detecção de malwares Android requer tipicamente o treinamento de modelos de aprendizado de máquina utilizando datasets que contém números expressivos de amostras (e.g., 100k, 1M) e características (e.g., 3k, 500k). Para reduzir a dimensionalidade dos datasets, pesquisadores vêm recorrentemente propondo diferentes métodos para a seleção de características (e.g., permissões, chamadas de API). Neste trabalho, avaliamos quatro métodos de seleção de características (SigPID, SigAPI, RFG, ALR) utilizando três datasets diferentes dos utilizados na avaliação original dos métodos. Os resultados indicam que há uma forte relação entre os datasets e os métodos de seleção, mesmo para tipos específicos de características (e.g., permissões).
Palavras-chave:
Seleção de Características, Malwares, Android
Referências
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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. JCVHT, 13.
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 Garcia-Castro, R., editor, The 32nd SEKE, pages 566–571. KSI Research Inc.
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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).
Liu, K., Xu, S., Xu, G., Zhang, M., Sun, D., and Liu, H. (2020). A review of Android malware detection approaches based on machine learning. IEEE Access, 8:124579.
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.
Masud, M. Z., Sahib, S., Abdollah, M. F., Selamat, S. R., and Huoy, C. Y. (2017). A comparative study on feature selection method for N-gram mobile malware detection. Int. J. Netw. Secur., 19(5):727–733.
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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.
Pontes, J., Costa, E., Rocha, V., Neves, N., Feitosa, E., Assolin, J., and Kreutz, D. (2021). Ferramentas de extração de caracter??sticas para análise estática de aplicativos android. In VI WRSeg.
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).
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. ACM.
Sahin, D. Ö., Kural, O. E., Akleylek, S., and K., E. (2021). A novel permission-based Android malware detection system using feature selection based on linear regression. Neural Computing and Applications, pages 1–16.
Salah, A., Shalabi, E., and Khedr, W. (2020). A lightweight Android malware classifier using novel feature selection methods. Symmetry, 12(5).
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 Cyber Security, Privacy and Networking, pages 347–356. Springer.
Sun, L., Li, Z., Yan, Q., Srisa-an, W., and Pan, Y. (2016). SigPID: significant permission identification for android malware detection. In 11th 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.
Wang, L., Gao, Y., Gao, S., and Yong, X. (2021). A new feature selection method based on a self-variant genetic algorithm applied to Android malware detection. Sym., 13(7).
Wang, W., Zhao, M., Gao, Z., Xu, G., Xian, H., Li, Y., and Zhang, X. (2019). Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions. IEEE Access, 7:67602–67631.
Zhou, Y. and Jiang, X. (2012). Dissecting Android malware: Characterization and evolution. In 2012 IEEE Symposium on Security and Privacy, pages 95–109.
Sahin, D., Kural, O., Akleylek, S., and Kilic, E. (2021a). A novel Android malware detection system: adaption of filter-based feature selection methods. Journal of Ambient Intelligence and Humanized Computing.
Sahin, D., Kural, O., Akleylek, S., and Kilic, E. (2021b). A novel permission-based Android malware detection system using feature selection based on linear regression. Neural Computing and Applications, pages 1–16.
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.
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. JCVHT, 13.
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 Garcia-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 Intelligent Tech. and Applications, page 311.
Han, J. and Kamber, M. (2006). Data mining: concepts and techniques, 2nd. University of Illinois at Urbana Champaign: Morgan Kaufmann.
Kouliaridis, V. and Kambourakis, G. (2021). A comprehensive survey on machine learning techniques for Android malware detection. Information, 12(5).
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).
Liu, K., Xu, S., Xu, G., Zhang, M., Sun, D., and Liu, H. (2020). A review of Android malware detection approaches based on machine learning. IEEE Access, 8:124579.
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.
Masud, M. Z., Sahib, S., Abdollah, M. F., Selamat, S. R., and Huoy, C. Y. (2017). A comparative study on feature selection method for N-gram mobile malware detection. Int. J. Netw. Secur., 19(5):727–733.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
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.
Pontes, J., Costa, E., Rocha, V., Neves, N., Feitosa, E., Assolin, J., and Kreutz, D. (2021). Ferramentas de extração de caracter??sticas para análise estática de aplicativos android. In VI WRSeg.
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).
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. ACM.
Sahin, D. Ö., Kural, O. E., Akleylek, S., and K., E. (2021). A novel permission-based Android malware detection system using feature selection based on linear regression. Neural Computing and Applications, pages 1–16.
Salah, A., Shalabi, E., and Khedr, W. (2020). A lightweight Android malware classifier using novel feature selection methods. Symmetry, 12(5).
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 Cyber Security, Privacy and Networking, pages 347–356. Springer.
Sun, L., Li, Z., Yan, Q., Srisa-an, W., and Pan, Y. (2016). SigPID: significant permission identification for android malware detection. In 11th 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.
Wang, L., Gao, Y., Gao, S., and Yong, X. (2021). A new feature selection method based on a self-variant genetic algorithm applied to Android malware detection. Sym., 13(7).
Wang, W., Zhao, M., Gao, Z., Xu, G., Xian, H., Li, Y., and Zhang, X. (2019). Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions. IEEE Access, 7:67602–67631.
Zhou, Y. and Jiang, X. (2012). Dissecting Android malware: Characterization and evolution. In 2012 IEEE Symposium on Security and Privacy, pages 95–109.
Sahin, D., Kural, O., Akleylek, S., and Kilic, E. (2021a). A novel Android malware detection system: adaption of filter-based feature selection methods. Journal of Ambient Intelligence and Humanized Computing.
Sahin, D., Kural, O., Akleylek, S., and Kilic, E. (2021b). A novel permission-based Android malware detection system using feature selection based on linear regression. Neural Computing and Applications, pages 1–16.
Publicado
12/09/2022
Como Citar
SOARES, Tainá et al.
Uma Análise de Métodos de Seleção de Características aplicados à Detecção de Malwares Android. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 22. , 2022, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 288-301.
DOI: https://doi.org/10.5753/sbseg.2022.225321.
