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 thedevelopment of more dependable and robust antispam filters. Machine learning methods of recent are beingused to successfully detect and filter spam emails. We present a systematic review of some of the popularmachine learning based email spam filtering approaches. Our review covers survey of the importantconcepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in thestudy background examines the applications of machine learning techniques to the email spam filteringprocess 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 incombating spam through the use machine learning techniques was done. Our review compares the strengthsand 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 effectivelyhandle the menace of spam emails