On Phishing URLs Detection Using Feature Extension

Phishing websites have diverse link outputs that are similar to benign links, making it difficult for users to distinguish them. As the main attack method for digital currency transactions in blockchain, phishing attacks also pose a huge threat to transactions. The existing methods for detecting phishing websites rely on the quality of URL feature extraction, and the extraction angle is becoming increasingly rigid. Therefore, this paper proposes a phishing URL detection model that utilizes feature extension. This method uses the TextRank algorithm to generate a feature extension library and embeds the extracted features into the URL to be detected. After the URL is vectorized, it is input into the two-layer classification network proposed in this paper to classify the website. This classifier consists of an upstream task Bert layer and a downstream task CNN layer. It is possible to simultaneously learn the comprehensive representation information and local feature information of URLs, effectively avoiding overfitting problems and improving the ability to identify phishing websites. Comparative experiments are conducted using a dataset of real phishing websites. The experimental results show that this model has higher accuracy and stability compared to other phishing website detection models. © 2024 IEEE.