Aplicabilidade e Impactos quanto a Adoção de Modelos de Classificação como Mecanismos Anti-phishing.
Resumo
Phishing websites are fake addresses that cheat the victims, passing by legitimate sites from banks or companies to obtain personal information without their consent. Looking to solve this problematic, several ways of defense were put into practice, among them the Machine Learning (ML). This article presents an study about ML utilization on malicious websites detection, reporting the methods used and concluding about their impact on precision and relevance.Referências
Abdelhamid, N., Thabtah, F., and Abdel-jaber, H. (2017). Phishing detection: A recent intelligent machine learning comparison based on models content and features. In 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), pages 72–77. IEEE.
Banu, M. N. and Banu, S. M. (2013). A comprehensive study of phishing attacks. International Journal of Computer Science and Information Technologies, 4(6):783–786.
Fadheel, W., Abusharkh, M., and Abdel-Qader, I. (2017). On feature selection for the prediction of phishing websites. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, pages 871–876. IEEE.
Fette, I., Sadeh, N., and Tomasic, A. (2007). Learning to detect phishing emails. In Proceedings of the 16th international conference on World Wide Web, pages 649–656. ACM.
Li, Y., Yang, Z., Chen, X., Yuan, H., and Liu, W. (2019). A stacking model using url and html features for phishing webpage detection. Future Generation Computer Systems, 94:27–39.
Banu, M. N. and Banu, S. M. (2013). A comprehensive study of phishing attacks. International Journal of Computer Science and Information Technologies, 4(6):783–786.
Fadheel, W., Abusharkh, M., and Abdel-Qader, I. (2017). On feature selection for the prediction of phishing websites. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, pages 871–876. IEEE.
Fette, I., Sadeh, N., and Tomasic, A. (2007). Learning to detect phishing emails. In Proceedings of the 16th international conference on World Wide Web, pages 649–656. ACM.
Li, Y., Yang, Z., Chen, X., Yuan, H., and Liu, W. (2019). A stacking model using url and html features for phishing webpage detection. Future Generation Computer Systems, 94:27–39.
Publicado
02/09/2019
Como Citar
DE BARROS, Mateus; DA SILVA, Carlo; DE MIRANDA, Péricles.
Aplicabilidade e Impactos quanto a Adoção de Modelos de Classificação como Mecanismos Anti-phishing.. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA E DE GRADUAÇÃO - SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 19. , 2019, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 39-42.
DOI: https://doi.org/10.5753/sbseg_estendido.2019.14003.
