Abstract: The rise in unsolicited emails, known as spam, has created an urgent need for more trustworthy and powerful antispam filters. Recent advances in machine learning techniques have enabled researchers and developers to effectively identify and filter spam emails. In this paper, we present a thorough analysis of several popular machine learning-based email spam filtering strategies. We provide an overview of key concepts, methods, effectiveness, and current research directions in spam filtering.
We begin by examining how top internet service providers (ISPs), including Gmail, Yahoo, and Outlook, apply machine learning techniques in their email spam filtering processes. We also describe the general process of email spam filtering and highlight the various ways researchers have applied machine learning to combat spam. Our evaluation compares the strengths and limitations of existing machine learning techniques and identifies unresolved challenges in spam filtering research. Based on our analysis, we recommend adopting deep learning and deep adversarial learning approaches to more effectively address the problem of spam emails in the future.
Keywords: Analysis of Algorithms, Machine Learning, Spam Filtering, Deep Learning, Neural Networks, Support Vector Machines (SVM), Naïve Bayes.
|
DOI:
10.17148/IJARCCE.2025.145100
