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Analysis of Naıve Bayes Algorithm for Email Spam
Filtering

The upsurge in the volume of unwanted emails called spam has created an intense need for the
development of more dependable and robust antispam filters. Machine learning methods of recent are being
used to successfully detect and filter spam emails. We present a systematic review of some of the popular
machine learning based email spam filtering approaches. Our review covers survey of the important
concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the
study background examines the applications of machine learning techniques to the email spam filtering
process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters.
Discussion on general email spam filtering process, and the various efforts by different researchers in
combating spam through the use machine learning techniques was done. Our review compares the strengths
and drawbacks of existing machine learning approaches and the open research problems in spam filtering.
We recommended deep learning and deep adversarial learning as the future techniques that can effectively
handle the menace of spam emails