Abstract

The expansion of the digital realm has caused a significant increase in digital communication, with emails being one of the most prominent forms of digital communication. The growth of email communication is applicable to professional and personal spaces, leaving many surface areas for attackers to exploit. Spam emails, a form of unsolicited emails that are often malicious to recipients, have been an ever-present problem for email users since the emails inception and the digital realms expansion on amplifying this problem. Email spam filters are a staple in email clients, and they are designed to identify these potentially malicious emails and alert the recipient of their malicious nature. Phishing is often the first step in malware-based attacks, and the fast-paced evolution of malware means that phishing attacks also get more sophisticated over time. A common solution to detecting malicious behaviour in the malware and spam domain is machine learning. We attempt to measure how evolution in the spam email space affects these machine-learning-based detection systems and how degradation in performance can be reduced.