Uma Análise Compreensiva e Exaustiva de Métodos de Seleção de Características para Detecção de Malware Android
Resumo
Os métodos de seleção de características são fundamentais para otimizar a predição e o uso de recursos em modelos de aprendizado de máquina. Neste estudo, avaliamos 17 métodos de seleção utilizando 10 conjuntos de dados distintos, sendo 11 métodos clássicos e 6 específicos para a detecção de malware em dispositivos Android. Surpreendentemente, métodos clássicos como o LASSO superaram os específicos, demonstrando excelente desempenho e capacidade de generalização na maioria dos conjuntos de dados testados.
Referências
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Alomari, E. S., Nuiaa, R. R., Alyasseri, Z. A. A., Mohammed, H. J., et al. (2023). Malware Detection Using Deep Learning and Correlation-Based Feature Selection. Symmetry, 15(1):123.
Azhagusundari, B., Thanamani, A. S., et al. (2013). Feature Selection Based on Information Gain. IJITEE, 2(2):18–21.
Bhat, P. and Dutta, K. (2022). A Multi-Tiered Feature Selection Model for Android Malware Detection Based on Feature Discrimination and Information Gain. Journal of King Saud University - Computer and Information Sciences, 34(10, Part B):9464–9477.
Cai, L., Li, Y., and Xiong, Z. (2021). JOWMDroid: Android Malware Detection Based on Feature Weighting with Joint Optimization of Weight-Papping and Classifier Parameters. Computers & Security, 100:102086.
Cao, C., Chicco, D., and Hoffman, M. M. (2020). The MCC-F1 Curve: A Performance Evaluation Technique for Binary Classification. arXiv preprint arXiv:2006.11278.
Chicco, D. and Jurman, G. (2020). The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation. BMC Genomics, 21(1):1–13.
Cohen, I., Huang, Y., Chen, J., Benesty, J., Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Pearson Correlation Coefficient. Noise Reduction in Speech Processing, pages 1–4.
Costa, E., Kreutz, D., Rocha, V., Leão, L., Sabóia, S., Neves, N., and Feitosa, E. (2022). FS3E: Uma Ferramenta para Execução e Avaliação de Métodos de Seleção de Características para Detecção de Malwares Android. In Anais Estendidos do XXII SBSeg, pages 151–158.
Darst, B. F. et al. (2018). Using Recursive Feature Elimination in Random Forest to Account for Correlated Variables in High Dimensional Data. BMC Genetics, 19:1–6.
Dhal, P. and Azad, C. (2022). A Comprehensive Survey on Feature Selection in the Various Fields of Machine Learning. Applied Intelligence, 52(4):4543–4581.
Fatima, A., Maurya, R., et al. (2019). Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning. In TSP, pages 220–223. IEEE.
Galib, A. H. and Hossain, M. (2020). Significant API Calls in Android Malware Detection (Using Feature Selection Techniques and Correlation Based Feature Elimination). In SEKE.
Islam, R., Sayed, M. I., Saha, S., et al. (2023). Android Malware Classification Using Optimum Feature Selection and Ensemble Machine Learning. IOTCPS, 3:100–111.
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Karaboga, D., Gorkemli, B., Ozturk, C., and Karaboga, N. (2014). A Comprehensive Survey: Artificial Bee Colony (ABC) Algorithm and Applications. AI Review, 42:21–57.
Konno, H. and Koshizuka, T. (2005). Mean-Absolute Deviation Model. IIE Transactions, 37(10):893–900.
Kurita, T. (2019). Principal Component Analysis (PCA). Computer Vision: A Reference Guide, pages 1–4.
Mahindru, A. and Sangal, A. (2019). Deepdroid: Feature Selection Approach to Detect Android Malware Using Deep Learning. In IEEE ICSESS, pages 16–19. IEEE.
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(5):1369–1411.
Meijin, L., Zhiyang, F., Junfeng, W., et al. (2022). A Systematic Overview of Android Malware Detection. Applied AI, 36(1).
Neves, N., Rocha, V., Kreutz, D., et al. (2023). Avaliação de Métodos de Seleção de Características de Amostras Android com a Ferramenta FS3E (v2). In Anais da XX ERRC, pages 139–144.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Ranstam, J. and Cook, J. A. (2018). LASSO Regression. Journal of British Surgery, 105.
Robnik-Šikonja, M. and Kononenko, I. (2003). Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning, 53:23–69.
Rocha, V., Bragança, H., Kreutz, D., and Feitosa, E. (2024). MH-FSF: um Framework para Reprodução, Experimentação e Avaliação de Métodos de Seleção de Características. [link].
Şahin, D. Ö., Kural, O. E., Akleylek, S., and Kılıç, E. (2023a). A Novel Android Malware Detection System: Adaption of Filter-Based Feature Selection Methods. JAIHC, pages 1–15.
Şahin, D. Ö., Kural, O. E., et al. (2023b). A Novel Permission-Based Android Malware Detection System Using Feature Selection Based on Linear Regression. Neural Comp Appl, pages 1–16.
Salah, A., Shalabi, E., and Khedr, W. (2020). A Lightweight Android Malware Classifier Using Novel Feature Selection Methods. Symmetry, 12(5):858.
Soares, T., Kreutz, D., Rocha, V., Costa, E., Leão, L., Pontes, J., Assolin, J., Rodrigues, G., and Feitosa, E. (2022). Uma Análise de Métodos de Seleção de Características Aplicados à Detecção de Malwares Android. In Anais do XXII SSBSeg, pages 288–301.
Soares, T., Siqueira, G., Barcellos, L., et al. (2021). Detecção de Malwares Android: Datasets e Reprodutibilidade. In Anais da XIX ERRC, pages 43–48, Porto Alegre, RS, Brasil. SBC.
Sthle, L. and Wold, S. (1989). Analysis of Variance (ANOVA). Chemometr Intell Lab., 6(4):259–272.
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. IEEE Computer Society.
Tallarida, R. J., Murray, R. B., Tallarida, R. J., and Murray, R. B. (1987). Chi-Square Test. Manual of Pharmacologic Calculations with Computer Programs, pages 140–142.
Zhao, K., Zhang, D., Su, X., and Li, W. (2015). Fest: A Feature Extraction and Selection Tool for Android Malware Detection. In IEEE ISCC, pages 714–720. IEEE.
Alomari, E. S., Nuiaa, R. R., Alyasseri, Z. A. A., Mohammed, H. J., et al. (2023). Malware Detection Using Deep Learning and Correlation-Based Feature Selection. Symmetry, 15(1):123.
Azhagusundari, B., Thanamani, A. S., et al. (2013). Feature Selection Based on Information Gain. IJITEE, 2(2):18–21.
Bhat, P. and Dutta, K. (2022). A Multi-Tiered Feature Selection Model for Android Malware Detection Based on Feature Discrimination and Information Gain. Journal of King Saud University - Computer and Information Sciences, 34(10, Part B):9464–9477.
Cai, L., Li, Y., and Xiong, Z. (2021). JOWMDroid: Android Malware Detection Based on Feature Weighting with Joint Optimization of Weight-Papping and Classifier Parameters. Computers & Security, 100:102086.
Cao, C., Chicco, D., and Hoffman, M. M. (2020). The MCC-F1 Curve: A Performance Evaluation Technique for Binary Classification. arXiv preprint arXiv:2006.11278.
Chicco, D. and Jurman, G. (2020). The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation. BMC Genomics, 21(1):1–13.
Cohen, I., Huang, Y., Chen, J., Benesty, J., Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Pearson Correlation Coefficient. Noise Reduction in Speech Processing, pages 1–4.
Costa, E., Kreutz, D., Rocha, V., Leão, L., Sabóia, S., Neves, N., and Feitosa, E. (2022). FS3E: Uma Ferramenta para Execução e Avaliação de Métodos de Seleção de Características para Detecção de Malwares Android. In Anais Estendidos do XXII SBSeg, pages 151–158.
Darst, B. F. et al. (2018). Using Recursive Feature Elimination in Random Forest to Account for Correlated Variables in High Dimensional Data. BMC Genetics, 19:1–6.
Dhal, P. and Azad, C. (2022). A Comprehensive Survey on Feature Selection in the Various Fields of Machine Learning. Applied Intelligence, 52(4):4543–4581.
Fatima, A., Maurya, R., et al. (2019). Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning. In TSP, pages 220–223. IEEE.
Galib, A. H. and Hossain, M. (2020). Significant API Calls in Android Malware Detection (Using Feature Selection Techniques and Correlation Based Feature Elimination). In SEKE.
Islam, R., Sayed, M. I., Saha, S., et al. (2023). Android Malware Classification Using Optimum Feature Selection and Ensemble Machine Learning. IOTCPS, 3:100–111.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
Karaboga, D., Gorkemli, B., Ozturk, C., and Karaboga, N. (2014). A Comprehensive Survey: Artificial Bee Colony (ABC) Algorithm and Applications. AI Review, 42:21–57.
Konno, H. and Koshizuka, T. (2005). Mean-Absolute Deviation Model. IIE Transactions, 37(10):893–900.
Kurita, T. (2019). Principal Component Analysis (PCA). Computer Vision: A Reference Guide, pages 1–4.
Mahindru, A. and Sangal, A. (2019). Deepdroid: Feature Selection Approach to Detect Android Malware Using Deep Learning. In IEEE ICSESS, pages 16–19. IEEE.
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(5):1369–1411.
Meijin, L., Zhiyang, F., Junfeng, W., et al. (2022). A Systematic Overview of Android Malware Detection. Applied AI, 36(1).
Neves, N., Rocha, V., Kreutz, D., et al. (2023). Avaliação de Métodos de Seleção de Características de Amostras Android com a Ferramenta FS3E (v2). In Anais da XX ERRC, pages 139–144.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Ranstam, J. and Cook, J. A. (2018). LASSO Regression. Journal of British Surgery, 105.
Robnik-Šikonja, M. and Kononenko, I. (2003). Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning, 53:23–69.
Rocha, V., Bragança, H., Kreutz, D., and Feitosa, E. (2024). MH-FSF: um Framework para Reprodução, Experimentação e Avaliação de Métodos de Seleção de Características. [link].
Şahin, D. Ö., Kural, O. E., Akleylek, S., and Kılıç, E. (2023a). A Novel Android Malware Detection System: Adaption of Filter-Based Feature Selection Methods. JAIHC, pages 1–15.
Şahin, D. Ö., Kural, O. E., et al. (2023b). A Novel Permission-Based Android Malware Detection System Using Feature Selection Based on Linear Regression. Neural Comp Appl, pages 1–16.
Salah, A., Shalabi, E., and Khedr, W. (2020). A Lightweight Android Malware Classifier Using Novel Feature Selection Methods. Symmetry, 12(5):858.
Soares, T., Kreutz, D., Rocha, V., Costa, E., Leão, L., Pontes, J., Assolin, J., Rodrigues, G., and Feitosa, E. (2022). Uma Análise de Métodos de Seleção de Características Aplicados à Detecção de Malwares Android. In Anais do XXII SSBSeg, pages 288–301.
Soares, T., Siqueira, G., Barcellos, L., et al. (2021). Detecção de Malwares Android: Datasets e Reprodutibilidade. In Anais da XIX ERRC, pages 43–48, Porto Alegre, RS, Brasil. SBC.
Sthle, L. and Wold, S. (1989). Analysis of Variance (ANOVA). Chemometr Intell Lab., 6(4):259–272.
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. IEEE Computer Society.
Tallarida, R. J., Murray, R. B., Tallarida, R. J., and Murray, R. B. (1987). Chi-Square Test. Manual of Pharmacologic Calculations with Computer Programs, pages 140–142.
Zhao, K., Zhang, D., Su, X., and Li, W. (2015). Fest: A Feature Extraction and Selection Tool for Android Malware Detection. In IEEE ISCC, pages 714–720. IEEE.
Publicado
16/09/2024
Como Citar
ROCHA, Vanderson; KREUTZ, Diego; BRAGANÇA, Hendrio; ASSOLIN, Joner; PINTO, Nicolas; FEITOSA, Eduardo.
Uma Análise Compreensiva e Exaustiva de Métodos de Seleção de Características para Detecção de Malware Android. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 24. , 2024, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 646-661.
DOI: https://doi.org/10.5753/sbseg.2024.241728.
