Enhancing Email Security: Optimizing Machine Learning with Bio-Inspired Metaheuristic Algorithms for Spam Detection

In today's digital era, email spam may lead to
phishing scams, malware infections, and even identity
theft, making email security a top priority. Spam
detection algorithms that are based on machine learning
have seen widespread application, and their effectiveness
may be improved with the help of bio-inspired

metaheuristic algorithms. This study provides, how bio-
inspired metaheuristic algorithms may be used in

conjunction with machine learning models for spam
identification. We talk about how to optimize the
parameters of machine learning models for spam
detection using genetic algorithms, particle swarm
optimization, and ant colony optimization. Additionally,
we discuss the significance of feature selection and
extraction in the development of effective spam detection
models. Finally, we shed light on how bio-inspired
metaheuristic algorithms may be used to improve email
security by strengthening spam detection systems'
precision and efficacy.