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Phishing attacks are one of the trending cyber attacks that apply sociallyengineered messages that are communicated to people from professional hackersaiming at fooling users to reveal their sensitive information, the most popularcommunication channel to those messages is through users emails. This paperpresents an intelligent classification model for detecting phishing emailsusing knowledge discovery, data mining and text processing techniques. Thispaper introduces the concept of phishing terms weighting which evaluates theweight of phishing terms in each email. The pre processing phase is enhanced byapplying text stemming and WordNet ontology to enrich the model with wordsynonyms. The model applied the knowledge discovery procedures using fivepopular classification algorithms and achieved a notable enhancement inclassification accuracy, 0.991 accuracy was achieved using the Random Forestalgorithm and 0.984 using J48, which is to our knowledge the highest accuracyrate for an accredited data set. This paper also presents a comparative studywith similar proposed classification techniques.

An intelligent classification model for phishing email detection