On Phishing URL Detection Using Feature Extension

Phishing is a common cybercrime event with great harm. Various phishing attacks have occurred repeatedly and have caused huge economic losses. With the booming development of blockchain and cryptocurrency, the huge amount of money in the field and the immature ecosystem have induced phishing attacks to flood the field in large quantities. Unfortunately, phishing has become the main means of attack in the field, posing a huge security threat to users’ digital assets. The existing methods for detecting phishing websites rely on the quality of uniform resource locator (URL) feature extraction, and the extraction angle is becoming increasingly rigid. Therefore, this article 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 article to classify the website. This classifier consists of an upstream task Bert layer and a downstream task convolutional neural network 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 data set of real phishing websites. The experimental results show that this model has higher accuracy and stability compared to other phishing website detection models.