Designing Spam Mail Filtering Using Data Mining by Analyzing User and Email Behavior

Abstract

Electronic Mail is the “killer network application”. It is ubiquitous and pervasive. In a relatively short timeframe, the Internet has become irrevocably and deeply entrenched in our modern society primarily due to the power of its communication substrate linking people and organizations around the globe. Much work on email technology has focused on making email easy to use, permitting a wide variety of information and information types to be conveniently, reliably, sent throughout the Internet. However, the analysis of the vast storehouse of email content accumulated or produced by individual users has received relatively little attention other than for specific tasks such as spam and virus filtering. Users in the email continuously receive spam and they get into trouble wasting their time and also harmful emails can cause harm to the computers. This thesis presents an implemented framework for data mining behavior models from email data. The EMT is a data mining tool kit designed to analyze email corpora, including the entire set of email sent and received by an individual user, revealing much information about individual users as well as the behavior of groups of users in an organization. A number of machine learning and anomaly detection algorithms are embedded in the system to model the user’s email behavior in order to classify email for a variety of tasks. There are different methods for detection of spam through email. The main goal is to develop a method that outperforms the existing methods in terms of detection of spam, ham and wrongly classified spam, i.e. need is to improve the accuracy of the proposed method compared to the other existing methods. The other goal is to implement the proposed algorithm for reducing the time. So, to recapitulate, this thesis also deals the accuracy and process timing based on prioritization of detecting email messages. The proposed method uses prioritization of process criterion which is unavailable in the earlier existing methods. It also uses the post-filtering concept which contributes for the enhancement of accuracy of the proposed method. Thus the proposed method, which we name as MAN is responsible for spam detection and outperforms Abstract xii the existing methods. This method also provides user convenient spam detection process. So, by using the concepts of post-filtering, process prioritization and different criterion in order to detect spam, the optimum accuracy for detecting spam will be possible.