Adaptive Email Spam Filtering using Recurrent Neural Networks

Email spam remains a persistent challenge in contemporary communication systems, demanding innovative approaches for effective filtering. This paper proposes an adaptive email spam filtering mechanism leveraging Recurrent Neural Networks (RNNs) to enhance accuracy and adaptability. Unlike traditional static filters, our approach dynamically adjusts its filtering criteria based on evolving spam patterns, thus ensuring robustness against emerging spamming techniques. The utilization of RNNs enables the model to learn temporal dependencies inherent in email content, allowing for more nuanced discrimination between legitimate and spam emails. Additionally, the proposed system incorporates a feedback loop mechanism to continuously update the model’s parameters, thereby ensuring adaptability to evolving spam characteristics. Through extensive experimentation on real-world email datasets, our approach demonstrates superior performance in terms of both accuracy and adaptability compared to existing spam filtering methods. This research presents a significant advancement in email spam filtering technology, offering a practical solution for combating the ever-evolving landscape of email spam.