Intelligent Phishing Email Detection System Based on Large Language Models

This project leverages the deep semantic understanding capabilities of large language models (LLMs) to identify phishing emails, breaking through the limitations of traditional detection methods. It ensures consistent and deterministic results across sessions via a semantic caching mechanism, providing an innovative solution for the cybersecurity field.

In the digital age, phishing emails cause billions of dollars in losses globally each year. Traditional detection methods such as rule-based filtering, feature-engineered machine learning, and blacklist mechanisms have issues like being easily bypassed, relying on manual features, and delayed response, making them difficult to handle complex phishing techniques.

This project is an open-source innovative solution with core innovations including semantic-level analysis, LLM-driven approach, semantic caching, and adaptive capabilities. The system architecture flow: Email Input → Preprocessing → Semantic Analysis → Cache Check → Decision Output; key components include preprocessing, semantic vectorization, LLM inference engine, cache layer, and decision module.

LLMs have deep semantic understanding capabilities, enabling context analysis, sentiment analysis, entity recognition, and logical reasoning. They can handle complex attack techniques such as brand impersonation, social engineering, link obfuscation, and content personalization, even when keyword filtering is evaded.

To address the needs of repeated detection of identical/similar emails, batch emails, and session consistency, semantic caching uses semantic hashing, similarity matching, result reuse, and consistency guarantee to reduce cost and latency, ensuring reliable and consistent results.

On the enterprise side, it can be integrated into email gateways, employee training, and generate security reports; on the personal side, it can be used as a plugin/extension to mark warnings; on the security research side, it can analyze trends, provide training materials, and study new techniques.

Cost and latency: semantic caching, layered detection, model optimization; False positives/negatives: adjustable thresholds, human-machine collaboration, feedback learning; Adversarial attacks: multi-model integration, combination with traditional features, continuous monitoring and updates.

This project breaks through traditional limitations and achieves semantic understanding and engineering practicality. Future directions include multi-modal detection, real-time learning, cross-language support, and deepfake detection; in terms of ecological integration, it will link with email service providers and security platforms, and participate in threat intelligence sharing.