A Review on Spam Detection using Deep learning Technique

Abstract

The exponential growth of digital communication and the pervasive nature of online platforms, the issue of spam has become a significant concern. Spam detection plays a crucial role in maintaining the integrity and efficiency of communication channels. This review provides a comprehensive survey of recent advancements in spam detection methodologies, focusing specifically on the application of deep learning techniques. The paper begins by offering an overview of traditional spam detection methods and their limitations, highlighting the need for more sophisticated approaches in the face of evolving spamming techniques. Subsequently, it delves into the foundations of deep learning and its relevance to the field of spam detection. Various deep learning architectures, including but not limited to convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep neural networks (DNNs), are discussed in detail, elucidating their strengths and weaknesses in the context of spam detection. The review critically analyses state-of-the-art research studies and methodologies, addressing key challenges such as feature extraction, model interpretability, and the handling of imbalanced datasets. It explores the integration of natural language processing (NLP) techniques within deep learning frameworks to enhance the detection of contextually complex spam content. Additionally, In this paper investigates the use of transfer learning and ensemble methods to improve model generalization across diverse spam datasets. the review sheds light on the implications of adversarial attacks on deep learning-based spam detection systems and proposes potential countermeasures. Ethical considerations, privacy concerns, and the trade-off between model accuracy and computational resources are also discussed in the broader context of deploying deep learning solutions for spam detection.