Effective Masked Attention Encoder Model for Website Hacking Prediction and Improved User Security

The growing sophistication of cyberattacks, particularly those targeting websites, has underscored the urgent need for intelligent, adaptive, and real‐time threat detection mechanisms. Traditional signature‐based approaches often fail to detect obfuscated or novel attacks, thereby compromising user and organizational security. This study addresses this gap by proposing a masked attention encoder (MAE) model integrated with an Extended Water Wave Optimization (EWWO) algorithm to predict and classify website hacking events with high accuracy. The model leverages masked attention mechanisms to extract contextual features from obfuscated data patterns while the EWWO algorithm optimally tunes the model's hyperparameters to enhance learning efficiency. Evaluations were conducted on an organizational dataset comprising diverse attack vectors, including malicious emails, endpoint malware, and harmful URL visits. The model's performance was benchmarked using precision, recall, and accuracy, achieving a detection accuracy of 98%, thereby outperforming state‐of‐the‐art methods. The proposed approach demonstrates strong potential for real‐time deployment in cybersecurity infrastructures, significantly enhancing threat detection and user protection.