Analysis on Phishing Detection Methods

With the popularization of the Internet, phishing has become one of the most threatening attacks in the field of network security, and the number of attacks continues to climb, the means are becoming increasingly complex, and it seriously threatens the privacy and property security of users. This paper aims to systematically analyze phishing detection methods to address this challenge and provide support for building a more secure network environment. The research focuses on four mainstream detection methods: list-based methods rely on black-and-white lists to quickly identify known threats, but are slow to react to new attacks; heuristic rule-based methods formulate rules through feature patterns, which have high interpretability but false positives may occur; machine learning based approach adapts to multiple phishing types through data learning, which is flexible but relies on feature engineering; Deep learning based approach uses neural networks to automatically extract complex features, with excellent generalization ability but insufficient interpretability. Future research needs to further promote the integration of technologies, enhance system transparency, develop lightweight models, and solve the problem of data scarcity.