Deep Learning Defense Against Sophisticated Spam
Traditional spam filters failed against AI-generated phishing attempts. Legacy rule-based systems had 18% false negative rates on sophisticated attacks targeting their user base.
We trained transformer-based classifiers on multilingual spam corpora, incorporating semantic understanding rather than keyword matching, with continuous learning from user feedback loops.
An adaptive spam filter using ensemble deep learning models that analyze content, sender reputation, and behavioral patterns to achieve near-zero false negatives.