A Multi-level User Identity Authentication Scheme Based on Environmental Detection

The rapid evolution of smart environments, coupled with the extensive adoption of Internet services, has fundamentally redefined traditional network boundaries. This transformation necessitates the development of more flexible, accurate, and resilient user identity authentication mechanisms. Conventional frameworks typically assume that users, devices, and authentication processes within a network are inherently trustworthy. However, these assumptions increasingly fall short in addressing modern cybersecurity challenges, thereby leaving systems susceptible to attacks. In response, this study introduces a multi-level identity authentication scheme that integrates facial recognition with continuous environmental detection to enhance security. The proposed framework establishes a bidirectional mutual authentication protocol between the user and the server, ensuring continuous verification beyond the initial login phase. In contrast to traditional methods that rely on static authentication mechanisms, this system persistently monitors the user’s environment and dynamically adjusts authentication parameters in response to environmental changes. Its effectiveness is rigorously validated using SVO logic, which demonstrates the scheme’s robustness against potential security threats. Furthermore, comprehensive security and performance evaluations confirm its feasibility and efficiency in protecting user identities within dynamic digital ecosystems.