Spam Email

Spam email detection remains a crucial challenge, with research focusing on improving the accuracy and efficiency of classification methods. Current efforts explore various machine learning approaches, including naive Bayes, support vector machines, decision trees, and increasingly, large language models like BERT and transformer-based architectures, often combined with techniques like TF-IDF for text representation. These advancements aim to create more robust and adaptable spam filters, reducing the burden on users and mitigating the risks associated with malicious emails, such as phishing and malware distribution. The development of efficient labeling techniques and methods for handling data poisoning further enhance the effectiveness and resilience of these systems.