A Survey of Existing E-mail Spam Filtering Methods Considering Machine Learning Techniques

Abstract

E-mail is one of the most secure medium for online communication and transferring data or messages through the web An overgrowing increase in popularity the number of unsolicited data has also increased rapidly To filtering data different approaches exist which automatically detect and remove these untenable messages There are several numbers of email spam filtering technique such as Knowledge-based technique Clustering techniques Learningbased technique Heuristic processes and so on This paper illustrates a survey of different existing email spam filtering system regarding Machine Learning Technique MLT such as Naive Bayes SVM K-Nearest Neighbor Bayes Additive Regression KNN Tree and rules However here we present the classification evaluation and comparison of different email spam filtering system and summarize the overall scenario regarding accuracy rate of different existing approaches