Phishing Detection Model Combining YOLOv11 on Web Logos

As a state-of-the-art approach, the application of deep learning for logo-based phishing website detection has been considered in recent researches. At present, many of the existing detection methods have achieved good results in conventional metrics such as accuracy, precision, recall, F1-score and time cost. This study proposes a comprehensive phishing detection model combing object detection on web logos. First, use YOLOv11 to capture the logo in the web page, then get the list of legitimate domain names corresponding to that category according to the obtained logo category, and finally match the string of url. Only if the domain name matches successfully will it be considered as a legitimate website, otherwise it will be a phishing site.We made a dataset containing five brands of logo to train and verify yolov11, obtained effective phishing URLs from Phishtank, and made a test set for YOLOv11 and a test set for phishing detection.The experimental results show that the average accuracy, accuracy, recall, F1 score and time cost of YOLOv11 model are 0.979, 0.99,0.973, 0.981 and 0.07s, respectively, indicating that YOLOv11 can quickly and accurately identify logo in web pages and give the judgment of phishing.