Email becomes the major source of communication these days. Most humans on the earth use email for their personal or professional use. Email is an effective, faster and cheaper way of communication. The importance and usage for the email is growing day by day. It provides a way to easily transfer information globally with the help of internet. Due to it the email spamming is increasing day by day. According to the investigation, it is reported that a user receives more spam or irrelevant mails than ham or relevant mails. Spam is an unwanted, junk, unsolicited bulk message which is used to spreading virus, Trojans, malicious code, advertisement or to gain profit on negligible cost. Spam is a major problem that attacks the existence of electronic mails. So, it is very important to distinguish ham emails from spam emails, many methods have been proposed for classification of email as spam or ham emails. Spam filters are the programs which detect unwanted, unsolicited, junk emails such as spam emails, and prevent them to getting to the users inbox. The filter classification techniques are categorized into two either based on machine learning technique or based on non-machine learning techniques. Machine learning techniques, such as Naïve Bayes, Support Vector Machine, Adaboost and decision tree etc. whereas non- machine learning techniques, such as black/white list, signatures, mail header checking etc. In this paper we review these techniques for classifying emails into spam or ham.
Ham, Spam, Email Spamming, Spam Filter, Email Spam