Phishing Website Detection

Phishing website detection aims to automatically identify fraudulent websites designed to steal user data, a critical task in cybersecurity. Current research heavily utilizes machine learning, employing various algorithms like XGBoost, multi-layer perceptrons, and large language models (including multimodal LLMs) to analyze website features such as HTML content, visual elements (logos, layout), and URL characteristics. These models are often trained on large datasets and deployed in real-time, either on-device or in cloud-based systems, to improve detection speed and accuracy. The development of robust and efficient phishing detection methods is crucial for mitigating the significant threat posed by these attacks.