Open AccessSpam classification by using association rule algorithm based on segmentation
Author(s) -
Shahad Suhail Najam,
Karim Hashim Al-Saedi
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.18486
Subject(s) - phishing , association rule learning , computer science , spamming , malware , the internet , key (lock) , harm , data mining , feature (linguistics) , tf–idf , forum spam , term (time) , spambot , machine learning , computer security , world wide web , linguistics , philosophy , physics , quantum mechanics , political science , law
Email is a most widespread and active communication technique. The major purpose behind the success of email is the vast availability, facility of utilize, and affordability. Therefore this technology has be a susceptible to malicious attacks; Email is the most frequently applied delivery technique for malware. E-mail spam is one of the major problems of the Internet today, and get financial harm to companies and individual users is uncomfortable. Spam mail can be harmful as they may include malware & links to phishing Web sites. So necessary to divide spam from mail messages to a separate folder. In this paper utilize one of datamining mechanism is association rule algorithm.in association rule; pattern discovered based on relationship between item-sets. The dataset utilized in proposed system is Enron dataset is divided into two parts: spam and non-spam. For extract features from dataset used Term Frequency Invers Term Frequency (TFIDF) method. For reduce dimensionality of feature space use Information Gain (IG) method.