Visible to the public Biblio

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2023-01-20
Wu, Fazong, Wang, Xin, Yang, Ming, Zhang, Heng, Wu, Xiaoming, Yu, Jia.  2022.  Stealthy Attack Detection for Privacy-preserving Real-time Pricing in Smart Grids. 2022 13th Asian Control Conference (ASCC). :2012—2017.

Over the past decade, smart grids have been widely implemented. Real-time pricing can better address demand-side management in smart grids. Real-time pricing requires managers to interact more with consumers at the data level, which raises many privacy threats. Thus, we introduce differential privacy into the Real-time pricing for privacy protection. However, differential privacy leaves more space for an adversary to compromise the robustness of the system, which has not been well addressed in the literature. In this paper, we propose a novel active attack detection scheme against stealthy attacks, and then give the proof of correctness and effectiveness of the proposed scheme. Further, we conduct extensive experiments with real datasets from CER to verify the detection performance of the proposed scheme.

2023-01-06
Zhang, Han, Luo, Xiaoxiao, Li, Yongfu, Sima, Wenxia, Yang, Ming.  2022.  A Digital Twin Based Fault Location Method for Transmission Lines Using the Recovery Information of Instrument Transformers. 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). :1—4.
The parameters of transmission line vary with environmental and operating conditions, thus the paper proposes a digital twin-based transmission line model. Based on synchrophasor measurements from phasor measurement units, the proposed model can use the maximum likelihood estimation (MLE) to reduce uncertainty between the digital twin and its physical counterpart. A case study has been conducted in the paper to present the influence of the uncertainty in the measurements on the digital twin for the transmission line and analyze the effectiveness of the MLE method. The results show that the proposed digital twin-based model is effective in reducing the influence of the uncertainty in the measurements and improving the fault location accuracy.
2020-07-10
Yang, Ying, Yu, Huanhuan, Yang, Lina, Yang, Ming, Chen, Lijuan, Zhu, Guichun, Wen, Liqiang.  2019.  Hadoop-based Dark Web Threat Intelligence Analysis Framework. 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). :1088—1091.

With the development of network services and people's privacy requirements continue to increase. On the basis of providing anonymous user communication, it is necessary to protect the anonymity of the server. At the same time, there are many threatening crime messages in the dark network. However, many scholars lack the ability or expertise to conduct research on dark-net threat intelligence. Therefore, this paper designs a framework based on Hadoop is hidden threat intelligence. The framework uses HDFS as the underlying storage system to build a HBase-based distributed database to store and manage threat intelligence information. According to the heterogeneous type of the forum, the web crawler is used to collect data through the anonymous TOR tool. The framework is used to identify the characteristics of key dark network criminal networks, which is the basis for the later dark network research.