Cross Page Recognition Methods for Encrypted Web Application Fingerprinting

The widespread implementation of the HTTPS protocol has greatly bolstered user privacy and data security. However, the widespread use of the HTTPS protocol has also provided criminals with a cloak to disseminate harmful content through websites, thereby undermining the integrity of the online environment. Web page fingerprinting has emerged as a highly popular method for web application identification. Yet, due to the frequent updates of web applications, existing methods struggle with accuracy issues. To tackle these challenges, this paper introduces a novel approach called CrossWP, which leverages cross-web page fingerprinting to enhance the security of the network environment. CrossWP aims at classifying web applications, which novelly constructs the cross web pages behavior sequences on handshake, request and response sequence. CrossWP uses the transformer model based on multi-sequence fusion to thoroughly learn and integrate these unique spatio-temporal sequence characteristics. This model can also capture the internal similarity of behavior sequence, and achieving high accuracy. The effectiveness of CrossWP is validated through closed-world and open-world evaluations, which involve identifying and classifying news websites, social websites, online video websites and e-commerce websites. The results indicate that CrossWP outperforms existing algorithms in terms of both robustness and accuracy.