Phishing Detection Using 1D-CNN and FF-CNN Models Based on URL of the Website
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62217-5_11
Reference15 articles.
1. Bahnsen, A.C., Bohorquez, E.C., Villegas, S., Vargas, J., González, F.A.: Classifying phishing URLs using recurrent neural networks. In: 2017 APWG Symposium on Electronic Crime Research (eCrime), pp. 1–8 (2017). https://doi.org/10.1109/ECRIME.2017.7945048
2. Cao, Y., Han, W., Le, Y.: Anti-phishing based on automated individual white-list, pp. 51–60 (10 2008). https://doi.org/10.1145/1456424.1456434
3. Chen, Y.S., Yu, Y.H., Liu, H.S., Wang, P.C.: Detect phishing by checking content consistency. In: Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014). pp. 109–119 (2014). https://doi.org/10.1109/IRI.2014.7051880
4. Ilango, H.S., Ma, M., Su, R.: A feedforward-convolutional neural network to detect low-rate dos in IoT. Eng. Appl. Artif. Intell. 114, 105059 (2022). https://doi.org/10.1016/j.engappai.2022.105059
5. Jain, A.K., Parashar, S., Katare, P., Sharma, I.: Phishskape: A content based approach to escape phishing attacks. Proc. Comput. Sci. 171, 1102–1109 (2020). https://doi.org/10.1016/j.procs.2020.04.118, third International Conference on Computing and Network Communications (CoCoNet’19)
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