Vinay Kumar Kasula

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.

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the attack surface for large-scale botnet operations. While previous research, including detailed analyses using Cowrie and IoTPOT frameworks, has studied IoT botnet behavior, these studies often rely on retrospective datasets, isolated protocol analyses, or hard-to-replicate setups. This paper addresses that gap with two main contributions: a structured review of ten influential IoT security studies from the USENIX Security Symposium and a confirmatory empirical experiment deploying Cowrie and IoTPOT honeypots simultaneously on a Microsoft Azure cloud-based virtual machine. Unlike earlier studies that focus on single protocols or large-scale environments, this work acts as a validation study, confirming well-known IoT botnet behaviors, including credential brute-force attacks, Mirai-style commands, and Telnet dominance, using real-time attack data collected from a reproducible, affordable cloud environment that simulates known IoT vulnerabilities (such as CVE-2016-10401, CVE-2017-17215, and CVE-2014-9222). Rather than revealing new attack methods, this study explicitly verifies the persistence of behaviors first documented almost ten years ago. The data indicates that attackers continue to exploit basic authentication flaws and reuse long-standing command sequences, confirming that core IoT vulnerabilities remain prevalent despite a decade of security research. It also highlights the ongoing gap between research progress and industry implementation. The analysis situates these findings within the broader evolution of IoT botnets, from early centralized command-and-control structures like Mirai to more resilient peer-to-peer networks that use anonymized channels and target high-wattage devices for power-grid manipulation. This study shows that small, cloud-based honeypots are valuable for continuous threat monitoring, model validation, and security assessments, providing a practical, reproducible approach for ongoing IoT security research.

A new design of privacy computing gateway stands as the solution to secure efficient interoperability between heterogeneous platforms. The growing importance of data privacy, along with rising collaborative data analysis operations, creates an immediate need for standardized privacy-preserving frameworks that are adaptable to diverse situations. A three-layered architecture consisting of application protocol and communication layers receives support from an Adaptation mechanism designed for compatibility between separate privacy computing systems. Testing of the framework uses standard machine learning methods together with horizontal and vertical federated learning using diverse data quantities and feature distribution patterns. The gateway achieves satisfactory model performance and protects data privacy integrity in combination with platform interoperability. area under the curve (AUC) along with F1 score metrics, proves that the proposed system reaches performance equivalence with centralized models when operating within privacy-limited environments. The research introduces an effective solution for securing cross-platform data sharing that will enable secure inter-sector collaboration in finance, healthcare, and government applications.

Blockchain technology allows decentralized cryptocurrencies to change digital finances by providing secure, pseudonymous transactions to users. Since blockchain ledgers operate in a public environment, users can face potential privacy risks due to the exposure of their transaction patterns. Conventional cryptocurrency systems use block generation for transaction confirmation, yet this process produces latency and impacts the real-time efficiency of transactions. This paper develops a proxy-assisted cryptocurrency payment system that employs blind signature principles to achieve better system privacy and enhanced speed. The core functionality of this proposed system aims to protect transaction secrecy as it speeds up confirmation processes. A proxy node handles transaction requests through blind signature protocols that guarantee data confidentiality as part of the methodology. The proposed system utilizes deep learning tools, which include recurrent neural networks (RNN), graph neural networks (GNN), and reinforcement learning (RL) to forecast confirmation results, identify scams, and control proxy functions dynamically. Research indicates that the introduced method substantially boosts privacy features, decreases transaction latencies, and enhances the security of all transactions by providing an encouraging roadmap for secure cryptocurrency systems that preserve privacy.