Publication Details

Abstract

Spam classification is a critical task in email filtering systems to distinguish between legitimate and spam emails. Traditional machine learning methods have been used for this purpose, but they often struggle to capture the complex patterns and variations in spam emails. In this paper, we propose a novel approach using Recurrent Neural Networks (RNNs) for spam classification. RNNs are well-suited for sequence modeling tasks like this, as they can capture dependencies between words in an email. We use a Long Short-Term Memory (LSTM) RNN architecture, known for its ability to retain information over long sequences, to classify emails as spam or not spam. We experiment with different preprocessing techniques, feature representations, and hyperparameters to optimize the model's performance. Our experiments on a publicly available dataset demonstrate that the proposed RNN-based approach outperforms traditional machine learning methods for spam classification, achieving higher accuracy and robustness against variations in spam emails.