Email Security Detection Solution Based on Multi-Scenario Classification

In the backdrop of the rapidly advancing global network informatization, cybercriminals frequently utilize a variety of phishing emails to launch attacks against numerous crucial organizations and information systems. Once these attacks succeed, they can inflict substantial damage on physical and digital systems as well as assets, resulting in the leakage of sensitive information, reputation impairment, and economic losses. The issue of how to detect phishing emails from a vast number of emails has long drawn significant attention. However, with the increasingly complex deception techniques of phishing emails, the existing solutions for detecting phishing attacks are no longer sufficient to tackle these problems. In this paper, a multi - scenario classification - based email security detection scheme is proposed. By analyzing the different stylistic features and deception differences of emails in various application scenarios, the collected email datasets are classified into multiple scenarios. Subsequently, the Long Short - Term Memory (LSTM) is employed to train the data under different classifications, and the trained model is used to classify phishing emails. The results demonstrate that the detection scheme proposed in this study exhibits relatively high accuracy.