Publisher
Springer Science and Business Media LLC
Reference25 articles.
1. Yang, P.; Zhao, G.; Zeng, P.: Phishing website detection based on multidimensional features driven by deep learning. IEEE Access 7, 15196–15209 (2019)
2. Abdulrahaman, M.D.; Alhassan, J.K.; Adebayo, O.S.; Ojeniyi, J.A.; Olalere, M.: Phishing attack detection based on random forest with wrapper feature selection method. Int. J. Inf. Process. Commun 7(2), 209–224 (2019)
3. Ali, W.; Ahmed, A.A.: Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm-based feature selection and weighting. IET Inf. Secur. 13(6), 659–699 (2019)
4. Ferreira, R.P.; et al.: Artificial neural network for websites classification with phishing characteristics. Soc. Netw. 7, 97–109 (2018)
5. Wei, B.; et al.: A deep-learning-driven light-weight phishing detection sensor. Sensors 19(19), 4258 (2019)
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