Spam Classification using Recurrent Neural Networks

Abstract

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