A Comprehensive Overview on Intelligent Spam Email Detection
Jayasree Nallabariki, T. Keerthi Chandana, D Sai Tejaswi, M Y Yesu Babu · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: Spam, usually referred to as unsolicited commercial or bulk e-mail, has recently become a major issue on the internet. Time, storage, and transmission bandwidth are all wasted by spam. Spam email has been a growing issue for years. Nowadays, automatic email filtering appears to be the most successful strategy for preventing spam. Only several years ago most of the spam could be reliably dealt with by blocking e-mails coming from certain addresses or filtering out messages with certain subject lines. Spammers started employing a number of cunning strategies to get beyond filtering techniques, such as utilizing random sender addresses and/or adding random characters to the message subject line's beginning or conclusion. Machine learning techniques now a days are used to automatically filter the spam e-mail in a very successful rate. Machine learning field is a subfield from the broad field of artificial intelligence, this aims to make machines able to learn like human. Understanding, observing, and providing knowledge about a statistical occurrence are all terms used here. In the first place, data collection and representation are typically problem-specific (i.e., for email messages), and in the second place, e-mail feature selection and feature reduction aim to lower the dimensionality (i.e. the number of features).Finally, the e-mail classification phase of the process finds the actual mapping between training set and testing set. Machine Learning approach includes lots of algorithms that can be used in e-mail filtering like Naïve Bayes, K-nearest neighbour, Support VSector Machine, classifiers. In conclusion, we try to summarize the performance results of the few machine learning methods in terms of spam precision and accuracy.