An intelligent classification model for phishing email detection)

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; 99.1% accuracy was achieved using the Random Forest algorithm
and 98.4% 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