Webpage Tampering Detection Method Based on BiGRU-CRF-RCNN

Xiangyu Fan, Jilong Yang, Wei Zhao, Jincheng Deng, Fangming Liu

Abstract: With the development of the Internet, cyber security events occur frequently, especially webpage tampering events account for a high proportion. In response to this phenomenon, this paper constructs a webpage tampering detection framework BCR. Based on the webpage to be detected, the webpage text data is segmented and extracted according to the webpage structure, the text features are extracted by using BiGRU model combined with context dependence, and then combined with the CRF to learn sequence state labeling named entities, the word vector is constructed by the extracted named entity and brought into the RCNN model for tampering detection. The experiment results show that the framework has achieved 95.37% precision, 95.35% recall and 95.34% F1-Score in webpage tampering detection, which is better than Textrank RCNN framework in webpage tampering detection. In practical application, it also achieved 95.13% precision and 93.25% recall.