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.