Advances in Spam Email Filtering: Integrating Deep Learning and Ensemble Methods

Abstract

Spam emails continue to grow in volume and sophis- tication each year, posing serious security risks to individuals, enterprises, and government systems. Modern attackers leverage linguistic obfuscation, AI-generated text, dynamic content injec- tion, and large-scale botnets to bypass traditional email filters. As a result, detecting malicious or unsolicited emails using con- ventional approaches has become increasingly challenging. This paper presents an expanded and in-depth survey of the evolution of spam filtering technologies, focusing on the integration of machine learning, ensemble learning, and advanced deep learning paradigms.

We analyze how classical approaches such as Naıve Bayes, Support Vector Machines, and Decision Trees laid the foundation for early spam detection, while highlighting their limitations in dealing with non-linear linguistic structures. The survey then transitions to exploring the transformative impact of modern deep learning architectures—particularly Convolutional Neural Networks, Recurrent Neural Networks, and Transformer-based models such as BERT—on capturing semantic, contextual, and syntactic properties of email text.

Furthermore, we investigate hybrid filtering frameworks that combine fast statistical models with high-capacity deep neural networks to achieve both computational efficiency and high detec- tion accuracy. We provide visual diagrams that illustrate practical implementation pathways, multi-stage filtering pipelines, and ensemble-based decision calibration. The paper concludes by identifying open research challenges related to explainable filter- ing, federated model training, multilingual filtering, adversarial robustness, and the need for energy-efficient deployment at scale. This comprehensive review establishes a clear understanding of the current landscape and encourages future contributions in the domain of intelligent spam filtering.