Abstract: Web application fingerprinting is a fundamental technique in cyberspace mapping,Web vulnerability exploitation,and cybersecurity situational awareness.Existing mainstream approaches primarily rely on manually crafted text-based rules and regular expression matching to identify Web applications.However,these methods face several limitations,such as the difficulty of rule extraction,challenges in distinguishing between similar sub-versions,and susceptibility to failure when page content changes.To address these issues,this paper proposes a Web application fingerprinting model based on a multi-level SimHash algorithm and digital feature snapshots.The method extracts representative page content that reflects core characteristics of Web applications,and maps it into high-dimensional digital fingerprints using multiple SimHash calculations to form computable and comparable feature snapshots.On this basis,a general fingerprinting model is constructed,with systematic definitions of its structure,key algorithms,and parameters.Furthermore,a practical implementation of the model is developed using HTML page content,and a series of experiments are conducted on various mainstream Web applications.Experimental results demonstrate that the proposed method outperforms traditional rule-based approaches in recognition accuracy,supports automatic fingerprint generation and sub-version identification,and exhibits robustness to page modifications to a certain extent.

Key words: Digital feature snapshot, Web application, Fingerprint recognition, Subversion recognition, Multi-level SimHash