The Internet of Things (IoT) is envisioned to link billions of diverse devices together. To safeguard privacy of user data, ensure authenticity of the data communicated between these devices, and guarantee their availability, robust authentication is needed. On the other hand, authentication techniques face several security challenges because IoT devices are resource-constrained (memory, battery, processor, etc.). To elaborate on this, we perform an extensive survey of authentication mechanisms in IoT, addressing the resource-constrained nature of devices, heterogeneity of networks, and prospective security challenges. We focus on five major classes of authentication protocols—namely, password-based, MAC-based, public identity-based, token-based, and biometrics-based protocols—and compare them based on communication and computation overhead, energy efficiency, scalability, and security. We also highlight the applicability of authentication mechanisms to four popular IoT platforms: RFID systems, smart grids, the Internet of Vehicles (IoV), and smart homes. Finally, we survey trending solutions such as implicit authentication using biometrics, blockchain-based identity management, and trusted computing. Beyond the qualitative comparison, this updated survey presents a formal, PRISMA-style review process including inclusion and exclusion criteria; a quantitative/information theoretic comparison of authentication classes (using Shannon entropy and confusion- matrix-derived metrics, the false acceptance rate, false rejection rate, equal error rate, and receiver operating characteristic reasoning); and discussion of higher-layer auth architectures including federated identity management, single sign-on (SSO), and OAuth/OpenID Connect that enable device-level protocols. Existing systematic reviews and auth schemes published since 2023 are also included to supplement previously compared device-centric works. Our analysis shows that token-based authentication currently offers the most balanced trade-off among security, scalability, and energy overhead for general-purpose IoT deployments, while biometric and implicit biometric schemes provide the strongest resistance to impersonation at higher computational cost and public-identity (asymmetric) schemes scale well but remain impractical for the most resource-constrained endpoints. In summary and to guide future work directly, the survey also outlines four promising directions towards which the community should further research: (i) hybrid schemes based on lightweight cryptography and behavioral or physiological biometrics; (ii) lightweight post-quantum authentication schemes, resistant to quantum computer attacks and deployable on constrained devices; (iii) continuous authentication and anomaly detection based on machine learning techniques; and (iv) federated and decentralized (blockchain-enabled) identity management solutions towards the minimization of single points of failure among heterogeneous IoT areas.

To address the challenges of data security governance under new conditions and align with technological trends in data security, this paper proposes a graph algorithm-based method for identifying key personnel with critical permissions in the practical applications of large state-owned enterprises. This method can uncover potential permission influence factors within the system and evaluate the weight of influence from different perspectives, providing highly interpretable identification results. To tackle the issue of detecting anomalous user and entity behaviors in data security governance, a user and entity behavior anomaly detection method based on Generative Adversarial Networks (GAN) is introduced. Experimental results show that the proposed method achieves higher precision, recall, and F1-score averages compared to baseline models; specifically, an average F1-score of 0.75 versus 0.72 for LSTM-based TadGAN, 0.62 for ARIMA, and 0.65 for a commercial UEBA baseline across the three evaluation datasets. A data security platform was designed and developed, which plays a significant role in reducing data security risks, assisting enterprise compliance, and promoting data development and utilization. This platform has been applied in various centralized data management projects and meets the big data processing requirements in secure environments, demonstrating strong application and promotional value.