A lightweight defense mechanism against next-generation of phishing emails using distilled attention-augmented BiLSTM

The current generation of large language models produces sophisticated social-engineering content that bypasses standard text screening systems in business communication platforms. Our proposed solution for mail gateway and endpoint deception detection operates in a privacy-protective manner while handling the performance requirements of network and mobile security systems. The MobileBERT teacher receives fine-tuning before its transformation into a BiLSTM model with multi-head attention which maintains semantic discrimination only with 4.5 million parameters. The hybrid dataset contains human-written messages together with LLM-generated paraphrases that use masking techniques and personalization methods to enhance modern attack resistance. The evaluation system uses five testing protocols which include human-only and LLM-only tests and two cross-distribution transfer tests and a production-like mixed traffic test to assess performance in native environments and across different distribution types and combined traffic scenarios. The distilled model maintains a weighted-F1 score difference of 1–2.5 points compared to the mixture split results of strong transformer baselines including ModernBERT, DeBERTaV3-base, T5-base, DeepSeek-R1 Distill Qwen-1.5B and Phi-4 mini while achieving 80–95% faster inference times and 95–99% smaller model sizes. The system demonstrates excellent performance in terms of accuracy and latency while maintaining a compact size which enables real-time filtering without acceleration hardware and supports policy-based management. The paper examines system performance under high traffic conditions and security measures for privacy protection and implementation methods for operational deployment. The research team will release all necessary code and training scripts and corpus splits to support security researchers who want to reproduce experiments and implement practical solutions.