Email spam filtering technique: challenges and solutions

• Main Authors: Md Yasin, Sharifah, Hadi Azmi, Iqbal
• Format: Article
• Published: Little Lion Scientific 2023
• Online Access: http://psasir.upm.edu.my/id/eprint/107653/; http://www.jatit.org/
• Institution: Universiti Putra Malaysia

For many years, practically all industries, from business to education, have used electronic mail for either personal or corporate communication. Spam, often known as unsolicited email, can be used to harm any user and computing resource by stealing important data. Email conversations frequently involve sensitive and private information. Emails are therefore useful to scammers since they able to use these facts for bad purposes. The primary goal of the attacker is obtaining personal data through deception email recipients into opening malicious links or downloading attachments. Cyberthreats have grown significantly during the past few years. The most common cybercrime that makes use of spam emails as a tool is phishing. Email phishing has caused significant identity and financial losses. Spam detection and filtering is a critical and important problem. There are numerous strategies that can be utilised to counter email spam. No technique has, however, been shown to be particularly successful. Some approaches, such as applying machine learning, have a very high potential for minimising the issues with email phishing. Reviewing filtering mechanisms, particularly those used in email, is crucial for understanding how they work and for spotting potential problems. Based on predetermined criteria, a number of papers on spam email were acquired from various digital sources. The most relevant papers that had just been released were chosen. Many researchers are interested in the methods used to filter spam and emails. One of the most significant and well-known methods for identifying and preventing spam is email filtering. These approaches have been contrasted. In order to identify phishing emails, this paper describes a machine learning (ML) approach. It talks about issues and anticipated future developments. In order to categorize phishing emails at various levels of crime, numerous ML models that have been suggested throughout the years are compared and reviewed.