Please use this identifier to cite or link to this item: http://dspacetest.aaup.edu/jspui/handle/123456789/3515
Title: AN INTELLIGENT CLASSIFICATION MODEL FOR PHISHING EMAIL DETECTION
Authors: Abuhasan, Abdelmunem$AAUP$Palestinian
Yasin, Adwan$AAUP$Palestinian
Keywords: phishing
data minin
email classification
Random Forest
J48
AAUP
Issue Date: Jul-2016
Publisher: International Journal of Network Security & Its Applications (IJNSA)
Abstract: 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.
URI: http://dspacetest.aaup.edu/jspui/handle/123456789/3515
Appears in Collections:Faculty & Staff Scientific Research publications

Files in This Item:
File Description SizeFormat 
1608.02196.pdf650.74 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Admin Tools