Today, phishing emails are the largest problem affecting internet services because they upset customers and cost businesses money. Methods that use the Natural Language Processing (NLP) principles also have many limitations and exhibit a flawed performance, especially regarding the non-English languages (such as the Arabic languages), given the lack of NLP for the Arabic language and the fact that this language has a rich vocabulary that delivers the same grammar and meaning. In this paper, viewed the previous models presented by other researchers, and also presented their RAPH model for the purpose of phishing detection. Where the model relies in its work on textual analysis of the content of e- mail messages and compares them with special datasets that include most of the commonly used words in electronic phishing. The results showed the effectiveness of the RAPH model, as it achieved a correct detection rate for phishing messages with a rate of (98.4%), while it achieved an error rate for legal messages with a rate of 7.5%.
Phishing email Detection, Natural Language Processing, Python Libraries, RAPH, NLP Features
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