Leading Prof.

Lan Zhang Ph.D. Professor and Ph.D. supervisor of ​University of Science and Technology of China.Excellent Youth.Research has been conducted on the Internet of Things, data privacy protection, and shared transactions.  

Ke XU Ph.D. Professor and Ph.D. supervisor of Tsinghua University Associate editor of Springer journal Research Interests: Next generation Internet, P2P systems, Internet of Things (IoT), Network virtualization and optimization etc.

Kebiao He Ph.D. Professor and Ph.D. supervisor of ​the National School of Cybersecurity, Wuhan University, and Executive Vice Dean of the Hubei Blockchain Technology Innovation Research Institute. Engaged in long-term research in the fields of cryptography, data security, blockchain technology, etc; Selected as a Global Highly Cited Scientist and a Chinese Highly Cited Scholar.

Research achievements.

Project Title: Research on IoT Security Technology Based on Blockchain
This project focuses on the key scientific issues and technical challenges in cross domain IoT system security, and has achieved a series of theoretical results for blockchain based cross domain IoT system security and trustworthiness.
The project team starts from large-scale cross domain node authentication management, data source trusted verification, dynamic group management, and anonymous control accountability, solving problems such as unreliable data sources, difficult balance between identity privacy and supervision, and achieving manageable and controllable service members. It supports efficient anonymous detection and precise traceability of specific violators, thereby ensuring the security of cross domain IoT services and providing theoretical and technical support for building a strong, secure, trustworthy, and efficient cross domain IoT system. This project has made breakthroughs in the key cryptographic theory and system design of cross domain IoT systems based on blockchain, which has promoted the innovative development of the country in network security, data security, and autonomous controllable blockchain, ensuring the political, economic, and social security of the country. The project research work proceeded according to plan, completed the project research tasks, and achieved the expected goals of the project.
Starting from the efficient and secure storage and analysis of network traffic status records, the project team has constructed a verifiable Bloom filter by combining traditional Bloom filters with Merkle trees. This ensures the security of data queries while achieving efficient storage of network status records; Through an adaptive Byzantine fault-tolerant consensus mechanism, efficient and stable consensus has been achieved for nodes with varying capabilities in dynamic non fully connected networks; On this basis, a real-time robust malicious traffic detection technology based on frequency domain features is proposed, which reduces feature redundancy in traffic records through data stream feature encoding, compresses feature size by more than 6 times, and combines frequency domain feature analysis to solve the problem of traditional stream level features lacking robustness and traditional packet level features unable to constrain feature size, resulting in high processing overhead and low processing throughput. It achieves the highest malicious traffic recognition accuracy in adversarial scenarios and improves detection throughput by 2 orders of magnitude compared to existing methods.
The project team starts from the differences in privacy information of different data, privacy definitions of different users, and the capabilities of different devices, and conducts semantic modeling for data and user needs. Based on semantic matching and rule learning methods, personalized privacy detection and privacy level grading are implemented. On this basis, a mechanism is designed to allocate different data processing and storage tasks reasonably on edge cloud devices according to the privacy level of data and the differences in end, edge, and cloud capabilities. On this basis, effective and verifiable data records can be provided while protecting the privacy of shared data on the blockchain, supporting identity verifiability of cross domain confidential data and conditional traceability based on flexible policies. A secure and verifiable cross domain data sharing platform can be provided. When different parties share data, they can obtain data usage authorization in accordance with contract regulations, as well as compliance data usage audits and mandatory accountability for violations after authorization.
After the project was approved, 188 high-quality papers were published in important journals and conferences related to the project, 21 invention patents were applied for, 3 software copyrights were obtained, 2 provincial and ministerial level natural science awards were won, "Key Technologies and Applications for Secure and Trusted End to End Transmission in connectionless Networks" won the first prize of the 2022 China Electronics Society Science and Technology Progress Award, "Edge Cloud Collaborative Multimodal Networks and Multimodal Data Mutual Annotation" won the Huawei Spark Award, and "Dataset Privacy Intersection Technology" won the first prize of the 2023 National Big Data and Computational Intelligence Challenge. On this basis, we will carry out industry university research cooperation, train 23 doctoral students and 16 master's students, and cultivate a group of blockchain technology professionals with active thinking, solid foundation, and innovative spirit. The master's and doctoral theses and project research content are directly related to key cryptographic technologies for blockchain privacy protection, data privacy protection technology based on secure multi-party computation, blockchain based authentication and access control schemes for remote medical services, blockchain based distributed identity management systems for social networks, design and implementation of anonymous voting schemes based on blockchain, and design and implementation of distributed anonymous credential schemes for the Internet of Vehicles. Real time network attack traffic detection technology is applied to Qianxin's NetEase anti denial of service system, achieving real-time attack traffic detection for 10G interfaces and enhancing network attack defense capabilities.