Email Spam Detection

Email spam detection aims to automatically filter unwanted emails, protecting users and systems from malicious content and wasted resources. Current research heavily utilizes large language models (LLMs) like BERT and GPT, alongside traditional machine learning algorithms such as Naive Bayes and Support Vector Machines, often incorporating techniques like hierarchical attention and zero-shot learning to improve accuracy and efficiency. This field is crucial for cybersecurity and user experience, with ongoing efforts focused on mitigating vulnerabilities, improving model robustness against adversarial attacks (like "BadNets"), and optimizing performance, particularly in resource-constrained environments or with limited training data.