EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense
Rapid evolution of multimodal phishing emails challenges traditional static detection methods, which suffer from poor adaptability and limited interpretability. Method: We propose a cognition-driven dynamic defense framework that (1) constructs a unified heterogeneous email graph integrating text, metadata, and embedded resources; (2) designs a collaborative reasoning mechanism between a Cognition Graph Neural Network (CGNN) and large language models (LLMs) for deep multimodal signal fusion; and (3) introduces an adversarial self-evolution mechanism—where red teams generate evasion samples and blue teams iteratively update and compress a memory knowledge base using failure experiences—to enable continual learning and strategy refinement. Results: Evaluated on both real-world and synthetic datasets, our approach significantly outperforms state-of-the-art baselines in accuracy, generalization, and interpretability, empirically validating the efficacy of dynamic evolutionary defense paradigms.