@article {10.3844/jcssp.2012.1099.1107,
article_type = {journal},
title = {Evolving Fuzzy Neural Network for Phishing Emails Detection},
author = {ALmomani, Ammar and Wan, Tat-Chee and Altaher, Altyeb and Manasrah, Ahmad and ALmomani, Eman and Anbar, Mohammed and ALomari, Esraa and Ramadass, Sureswaran},
volume = {8},
number = {7},
year = {2012},
month = {Jun},
pages = {1099-1107},
doi = {10.3844/jcssp.2012.1099.1107},
url = {https://thescipub.com/abstract/jcssp.2012.1099.1107},
abstract = {One of the broadly used internet attacks to deceive customers financially in banks and agencies is unknown “zero-day” phishing Emails “zero-day” phishing Emails is a new phishing email that it has not been trained on old dataset, not included in black list. Accordingly, the current paper seeks to Detection and Prediction of unknown “zero-day” phishing Emails by provide a new framework called Phishing Evolving Neural Fuzzy Framework (PENFF) that is based on adoptive Evolving Fuzzy Neural Network (EFuNN). PENFF does the process of detection of phishing email  depending on the level of features similarity between body email and URL email features. The totality of the common features vector is controlled by EFuNN to create rules that help predict the phishing email value in online mode. The proposed framework has proved its ability to detect phishing emails by decreasing the error rate in classification process. The current approach is considered a highly compacted framework. As a performance indicator; the Root Mean Square Error (RMSE) and Non-Dimensional Error Index (NDEI) has 0.12 and 0.21 respectively, which has low error rate compared with other approaches Furthermore, this approach has learning capability with footprint consuming memory."},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}