An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-08530-7_42
Reference13 articles.
1. Wei, W., Ke, Q., Nowak, J., Korytkowski, M., Scherer, R., Woźniak, M.: Accurate and fast URL phishing detector: a convolutional neural network approach. Comput. Netw. 178 (2020). https://doi.org/10.1016/j.comnet.2020.107275
2. Feng, J., Zou, L., Yang, Y., Han, O., Zhou, J.: Web2Vec: phishing webpage detection method based on multidimensional features driven by deep learning. IEEE Access. 8, (2020). https://doi.org/10.1109/ACCESS.2020.3043188
3. Xiao, X., Zhang, D., Hu, G., Jiang, Y., Xia, S.: CNN–MHSA: a Convolutional Neural Network and multi-head self-attention combined approach for detecting phishing websites. Neural Netw. 125, 303–312 (2020). https://doi.org/10.1016/j.neunet.2020.02.013
4. Adebowale, M.A., Lwin, K.T., Hossain, M.A.: Intelligent phishing detection scheme using deep learning algorithms. J. Enterp. Inf. Manag. (2020). https://doi.org/10.1108/JEIM-01-2020-0036
5. Liu, D., Lee, J., Wang, W., Wang, Y.: Malicious Websites Detection via CNN based Screenshot Recognition*. 115–119 (2019)
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