DMPA: A Compact and Effective Pipeline for Detecting Multiple Phishing Attacks

In recent years, deep learning has significantly advanced the field of phishing defense, but most methods are typically effective only against specific types of attacks, such as phishing emails. Additionally, the large and complex nature of these models often makes them difficult to integrate efficiently into devices. To address these issues, we propose a pipeline for Detecting Multiple Phishing Attacks (DMPA) that combines fine-tuning of a pretrained model with adversarial training. We first apply a unique fine-tuning strategy to optimize resource usage, and then employ adversarial training across multiple types of phishing text attacks. By introducing perturbations to the model’s embedding vectors, we generate a large number of adversarial examples to enhance the accuracy and robustness of our model. We constructed a dataset of 100K high-quality samples and compared our method against the latest approaches from various aspects. The results show that our method surpasses all existing state-of-the-art models in overall performance. Ablation experiments further demonstrate that adversarial training significantly improves the model’s performance, confirming the effectiveness of our approach. Additionally, we have packaged the trained model into a complete pipeline, making it easier to integrate into devices. The code is made available at https://github.com/donghuang-cyber/PHISHINGDENFESE/tree/main