Spam Detection Using Machine Learning

Abstract

Deep learning has emerged as a powerful technique for spam detection due to its ability to automatically learn relevant features from raw data. This abstract presents an overview of deep learning approaches for spam detection and highlights their effectiveness in combating the ever-evolving landscape of spam. The challenges of spam detection, includes the use of sophisticated techniques by spammers to evade traditional rule-based filters. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown promise in addressing these challenges by automatically extracting high-level features from spam messages. The Spam Detection Using Machine Learning in this paper employs the Deep Learning model Bi-GRU to detect the spam messages. The advantages of this approach, is that it has the ability to handle large-scale datasets and their potential for transfer learning across different spam detection tasks. It highlights the role of deep learning in enhancing feature representation, model generalization, and the overall accuracy of spam detection systems