Enhanced discussion on different techniques of spam detection

Now days, spam mails is one of the major issues over mailboxes. As it consumes memory and wastes bandwidth of mailboxes, it is mandatory to prevent spam mails. It also wastes time of users. Sometimes, frustrated user even ignores some important mails which are not spam mails. Spamming is proliferating because sending mails has become so cheap. So far as there is no perfect technique to effectively filter out spam mails from mailboxes. Machine learning as well as non-machine learning techniques has been applied to detect spam. In this paper, machine learning techniques such as rule-based approach as well as non-machine learning techniques such as whitelist and blacklist are compared. Performance and results are compared of both techniques and evaluation is done by already existing datasets. Both techniques have a satisfactory performance but accuracy is more witnessed in case of machine learning techniques.