Enhancing Email Safety: Harnessing ML, DL, and LLM Models for Spam Detection

In today's digital landscape, email remains vital for communication in both personal and professional realms, yet spam continues to compromise user privacy and productivity. This paper explores an advanced approach to enhancing email security by integrating machine learning (ML), deep learning (DL), and Large Language Models (LLMs). Our solution leverages ML algorithms, such as Logistic Regression and Support Vector Machines (SVM), to classify emails by analyzing content, headers, and sender details. To adapt to evolving spam tactics, we employ deep learning models like Convolutional Neural Networks (CNNs) to capture complex patterns in email data. The paper also investigates the novel application of LLMs, which offer innovative strategies for addressing spam beyond traditional heuristic methods. This paper attempts to train these models using a dataset comprising spam and ham emails. LLMs have swiftly reshaped the landscapes of business, consumer behavior, and academia, demonstrating transformational potential for society. Using a dataset of spam and ham emails, we demonstrate that this combined approach offers a robust and adaptable framework for improving email safety, illustrating the transformative potential of LLMs in enhancing spam detection.