  Phishing attacks are one of the trending cyber attacks that apply socially
engineered messages that are communicated to people from professional hackers
aiming at fooling users to reveal their sensitive information, the most popular
communication channel to those messages is through users emails. This paper
presents an intelligent classification model for detecting phishing emails
using knowledge discovery, data mining and text processing techniques. This
paper introduces the concept of phishing terms weighting which evaluates the
weight of phishing terms in each email. The pre processing phase is enhanced by
applying text stemming and WordNet ontology to enrich the model with word
synonyms. The model applied the knowledge discovery procedures using five
popular classification algorithms and achieved a notable enhancement in
classification accuracy, 0.991 accuracy was achieved using the Random Forest
algorithm and 0.984 using J48, which is to our knowledge the highest accuracy
rate for an accredited data set. This paper also presents a comparative study
with similar proposed classification techniques.
