A Comparative Study of Deep Learning Approaches for Spam Detection

Spam identification is critical in Morden digital communication systems such as email, SMS, social media, and online platforms. The increasing proliferation of unwanted and hazardous messages such as spam, phishing, and scam material poses a severe threat to user privacy and cybersecurity. Deep learning (DL) algorithms can identify automatically learn intricate representations from large-scale various data sources, including reviews, SMS, and email data. They have become powerful alternatives that reflect modern spam qualities across several platforms and languages are sparse. This article presents an exhaustive review of deep learning-based spam detection approaches.

The dataset The lack of dynamic, multilingual, and real-world data limits adaptability and the ability to deal with evolving spam. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, hybrid models, and transformer-based techniques are among the most commonly used designs discussed. Explainable AI (XAI) approaches are not well-integrated to provide clear and understandable explanations for spam detection options. In order to give future research paths for constructing reliable and intelligent spam detection systems, the study discusses datasets, assessment metrics, obstacles, and unfilled research gaps.

The goal of this survey is to provide an organized summary of deep learning techniques. The design of an effective, flexible, and userfriendly deep learning-based spam detection framework that can precisely identify a variety of changing spam messages in real-world communication systems while maintaining low figuring overhead and high robustness is the main issue in this field of study.