| dc.description.abstract | Email remains a cornerstone of digital communication, facilitating fast and efficient
Internet communication. The growing reliance on email has led to numerous issues
caused by spam. Spam infiltrates personal and professional accounts, posing threats
ranging from phishing scams to malware dissemination. There is an urgent need to
develop stronger dependable antispam filters is crucial to protect users from spammers
evolving tactics and maintain the integrity and safety of digital communication
channels. Spam emails can now be efficiently recognized and filtered thanks to recent
developments in machine learning algorithms like hybrid approaches, corporate email
systems, ad anti-spam software solutions. We provide an in-depth examination of
several popular machine learning-based email spam filtering methods. An outline
of the main concepts, approaches, efficacy, and future directions of spam filtering
research is provided in our review. Machine learning-based spam filters like naive bayes,
support vector machines, decision trees, neural networks, ensemble methods and their
variants are our primary emphasis. We present the results of a thorough analysis that
includes a survey of relevant concepts, efforts, efficacy, and recent developments. Our
evaluation rigorously compares the benefits and limitations of current machine-learning
techniques with the unresolved issues in spam filtering research. We conclude by
discussing performance evaluation measures of machine learning-based filters and
explore challenges and future directions of the latest developments. | en_US |
