Detection and Prediction of Spam Emails Using Machine Learning Models

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Author(s): Salma P. Z (NSS College of Engineering, Kerala, India)and Maya Mohan (NSS College of Engineering, Kerala, India)
Copyright: 2021
Pages: 18
Source title: Handbook of Research on Cyber Crime and Information Privacy
Source Author(s)/Editor(s): Maria Manuela Cruz-Cunha (Polytechnic Institute of Cávado and Ave, Portugal)and Nuno Mateus-Coelho (Lusófona University, Portugal)
DOI: 10.4018/978-1-7998-5728-0.ch011

Keywords: Cyber and Network Security / Information Science Reference / Information Security & Privacy / Security & Forensics

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Abstract

One of today's important means of communication is email. The extensive use of email for communication has led to many problems. Spam emails being the most crucial among them. It is one the major issues in today's internet world. Spam emails contain mostly advertisements and offensive content, which are often sent without the recipient's request and are generally annoying, time consuming, and wasting space on the communication media's resources. It creates inconveniences and financial loss to the recipients. Hence, there is always the need to filter the spam emails and separate them from the legitimate emails. There are a lot of content-based machine learning techniques that have proven to be effective in detecting and filtering spam emails. Due to a large increase in email spamming, the emails are studied and classified as spam or not spam. In this chapter, three machine learning models, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BLSTM), are used classify the emails as spam and benign.

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Detection and Prediction of Spam Emails Using Machine Learning Models

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