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Guardgrid: A Queriable and Privacy-Preserving Aggregation Scheme for Smart grid via Function Encryption

IEEE Internet of Things Journal 2025年第11期12卷 17622-17633页

作者： Yu, Pengfei Huang, Weicong Zhang, Ran Qian, Xinyuan Li, Hongwei Chen, Hanxiao State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology China Electric Power Research Institute Nanjing210003 China University of Electronic Science and Technology of China Chengdu611731 China

Smart grids have revolutionized electricity management by leveraging real-time consumption data, enabling more efficient power control through advanced algorithms. However, this transformation raises significant privacy and security concerns due to the extensive collection of user data. Current solutions face challenges such as aggregator gateway misbehavior, lack of support for function queries, and the need to balance privacy with efficiency. In this paper, we propose FEHH, a novel scheme that ensures both privacy preservation and verifiable aggregation. It allows multiple aggregators to perform inner-product computations on encrypted data while safeguarding the aggregated results from the aggregator. Additionally, it supports verification of aggregated data's correctness using Linear Homomorphic Hash. Building on FEHH, we introduce Guardgrid, a privacy-preserving aggregation scheme for smart grids that inherits FEHH's core features and adds support for essential arithmetic operations necessary for function queries. This allows cloud servers to respond to queries from either the control center or users without compromising data confidentiality. Experimental results show that the encryption overhead of Guardgrid is only 7% of that of the PPDA scheme, and its communication overhead is 123× less. These results demonstrate that Guardgrid significantly reduces computation and communication costs, providing a more sustainable and cost-effective smart grid solution. © 2014 IEEE.

关键词： Differential privacy

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Abnormal Network Traffic Detection Algorithm Based on Improved Convolutional Neural Network

Abnormal Network Traffic Detection Algorithm Based on Improv...

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2024 International Conference on Interactive Intelligent Systems and Techniques, IIST 2024

作者： Chen, Lu Zhang, Tao Ma, Yuanyuan Chen, Mu State Grid Smart Grid Research Institute Co. Ltd Protection and Monitoring Technology Nanjing210003 China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China

ISBN: (纸本)9798350374421

With the increasing complexity, automation, and intelligence of network attacks, new types of attacks are constantly emerging in the network, posing great challenges to network attack detection and timely response based on prior rules. In order to more effectively and accurately identify abnormal traffic, a network abnormal traffic detection algorithm based on improved convolutional neural networks is proposed. The algorithm first inputs the key features of traffic anomaly monitoring, converts them into color images through visualization technology, extracts higher dimensional features, and then uses neural structure search technology to find a lightweight convolutional network structure with high accuracy and less time consumption. The optimal model is used to output the type of traffic anomaly. The results indicate that the algorithm has a higher accuracy and the shortest classification time for a single image. © 2024 IEEE.

关键词： Convolutional neural networks

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Construction and application of high-order tensor network models for large-scale electric power grids

Construction and application of high-order tensor network mo...

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2024 International Conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Qiao, Junfeng Xu, Min Zhou, Aihua Peng, Lin Pan, Sen Yang, Pei State Grid Laboratory of Power cyber-Security Protection & Monitoring Technology State Grid Smart Grid Research Institute Co.LTD Jiangsu Nanjing210003 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

With the continuous construction and promotion of ultra-high level voltage lines, the geographical scope of China's power grid is constantly expanding. At the same time, the complexity of the topology structure of the power grid, the types and quantities of electrical equipment, and the operational status of the power grid are constantly expanding and becoming more complex. However, the current data description of the operational evolution trend of large-scale power grids still remains at the stage of separately describing from the perspective of multiple application systems. In response to this issue, the article proposes a method for constructing a high-order tensor network model for large-scale power grid analysis applications. Due to the large number of equipment components in the power grid and their extremely high frequency of change, the use of intuitive description methods cannot meet the fast and real-time expression requirements of the power grid operation situation. Using the equivalence method to replace various entities in the power grid, and using numerical methods to replace a large number of components such as lines and switches in the power grid. Based on this, the vector form of power system operation data is transformed into a three-dimensional tensor form, thereby retaining the spatial distance of power system nodes and reflecting the spatial correlation of power system operation data. At the same time, it can be used as input for typical power analysis calculation models for power system analysis. © 2025 SPIE.

关键词： Photons

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Research on Heterogeneous Computing technology Based on Domestic CPUs and Reconfigurable Chips 4

Research on Heterogeneous Computing Technology Based on Dome...

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4th International Conference on Intelligent power and Systems, ICIPS 2024

作者： Guan, Xiaojuan Wang, Chen Zhou, Cheng Zhu, Sheng China Electric Power Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China

ISBN: (纸本)9798331532765

In view of the performance and security problems existing after the replacement of specialized security protection equipment for power grids with domestic software and hardware, and in response to the security protection requirements of different application scenarios, the research on heterogeneous fusion computing technology based on domestic CPUs and reconfigurable chips is proposed. The optimal mapping of national secret algorithms, isolation exchange, protocol processing, and data analysis on reconfigurable chips is realized, supporting the parallel computing of special processors for scene acceleration and domestic CPUs, and meeting the requirements of different network security analysis and computing scenarios in the power system. © 2024 IEEE.

关键词： Digital storage

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Research and Application of Reconfigurable Computing Approaches in power Network security Processing 4

Research and Application of Reconfigurable Computing Approac...

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4th International Conference on New Energy and power Engineering, ICNEPE 2024

ISBN: (纸本)9798331516086

Given the issue that certain key components of the dedicated security protection equipment for the power system rely heavily on large-scale imported FPGA chips and specialized integrated circuits, and mature domestic alternatives and solutions have not yet been formed, this paper proposes the application concept of reconfigurable computing technology in the process of high-performance network security processing. The reconfigurable CGRA chip is led by China, with independently controllable technology, combining energy efficiency and flexibility, enabling efficient processing of computation-intensive tasks and completing the substitution for foreign FPGA chips. © 2024 IEEE.

关键词： Isolation Switching Multi-level Configuration Network security Processing Reconfigurable Computing

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Automated security Penetration Testing Based on Machine Learning 4

Automated Security Penetration Testing Based on Machine Lear...

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4th International Conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Zhang, Peng Wang, Wenhui Zhang, Daojuan Wu, Hongbin Han, Longxi Zhao, Qi China Electric Power Research Institute Co. Ltd. State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Beijing China

ISBN: (纸本)9798331534622

Penetration testing is a crucial measure for preventing cyberspace attacks. Typically, penetration testing is conducted manually, which presents challenges such as reliance on experience, high costs, and low efficiency. This study proposes an automated penetration testing method that integrates machine learning algorithms with Metasploit to achieve low-code generation and execution of penetration testing cases. This approach enhances testing efficiency, reduces costs, and mitigates dependence on experience. The study introduces a decision-making algorithm to facilitate the selection of exploit files during the generation of penetration testing cases and leverages Metasploit to implement basic penetration testing functionalities. Experimental results show that the algorithm achieves an accuracy of 73.16%, and the method successfully meets the objective of automated penetration testing. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on multidimensional data analysis method of electric power based on tensor network model

Research on multidimensional data analysis method of electri...

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2024 International Conference on Electronics, Electrical and Information Engineering, ICEEIE 2024

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

Tensor networks can describe entity objects from multiple dimensions to more accurately reflect all the features of the entity. There are a large number of physical devices in the power grid, with multiple basic attributes and frequent updates of device status. The use of tensor network models can effectively characterize the equipment in the power grid and the operational situation of the power grid. This paper proposes the use of tensor network models to analyze the equipment and dynamic characteristics of the power grid. Construct a tensor network model from multiple dimensions such as device type, voltage level, affiliation, and device status. In response to the large number and scale of equipment in the power grid, analyzing the characteristics of concentrated application scenarios, reducing the dimensions of tensor network models and highlighting commonly used data description dimensions to meet the needs of power grid applications. Based on this model, carry out application verification such as power supply reliability analysis and energy loss analysis. Extract corresponding data resources from different coordinate dimensions, and then calculate the inner product of the data from different dimensions. Based on the calculation results, perform neural network correlation analysis on different data resources, and solve the analysis results and solutions. © 2024 SPIE.

关键词： Energy dissipation

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APT Attack Investigation via Fine-grained Sequence Construction and Learning 3

APT Attack Investigation via Fine-grained Sequence Construct...

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3rd International Symposium on Computer technology and Information Science, ISCTIS 2023

作者： Wu, Tianqi Lv, Zhuo Zhang, Daojuan Qian, Kexiang Wang, Ming State Grid Smart Grid Research Institute Co. Ltd China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Sgri Power Grid Digitizing Technology Department State Grid Henan Electric Power Research Institute State Grid Laboratory of Power Monitoring System Cyber Security Technical Standard Verification

ISBN: (纸本)9798350325386

APT attack investigation aims to provide the security investigators a causal subgraph of the whole causal graph, so that they can easily analyze attacks. However, existing methods either output subgraphs that miss critical attack steps, or are too large and thus challenging to utilize. To address these limitations, we propose a new APT attack investigation approach based on fine-grained sequence construction and learning. Specifically, our approach is built upon the ATLAS framework, and constructs more attack sequences with a finer granularity. It then learns the attack behavior patterns from these constructed sequences. During inference, when presented with an attack symptom, our approach first predicts attack-related nodes in the causal graph and then constructs the causal subgraph based on these nodes. To evaluate our method, we conduct experiments using a simulated environment and four real attacks. The results demonstrate the effectiveness of the proposed approach compared to the state-of-the-art method ATLAS. © 2023 IEEE.

关键词： attack investigation machine learning sequence construction

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Research on new power business threat modeling method based on bayesian networks 3

Research on new power business threat modeling method based ...

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3rd International Conference on Electronic Information Engineering and Data Processing, EIEDP 2024

作者： Li, Yong Zhang, Tao Shao, Zhipeng Chen, Mu Ma, Yuanyuan State Grid Smart Grid Research Institute Co. Ltd. Nanjing210003 China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China

ISBN: (纸本)9781510680531

The emerging power system, predominantly reliant on renewable energy sources, drives the future diversification of end-use energy structures. However, within this new power system, business terminals encounter various access risks. Terminals within unmanned aerial vehicle (UAV) power inspection systems, distributed photovoltaic business, and vehicular networking are susceptible to network attacks, exposing them to risks of information leakage. To address the security threats prevalent in the emerging power industry, this paper proposes a Bayesian network-based threat modeling approach. Building upon this, a probabilistic graphical model-based threat risk assessment scheme is presented. Furthermore, typical interaction scenarios of novel power system operations are selected to demonstrate the effectiveness of the model by employing Bayesian networks for modeling and simulation validation. © 2024 SPIE.

关键词： Bayesian networks

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Terminal Anomaly Discovery technology Based on Service Behavior Deviation 5th

Terminal Anomaly Discovery Technology Based on Service Behav...

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5th International Conference on 3D Imaging Technologies—Multidimensional Signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Chen, Lu Zhang, Tao Ma, Yuanyuan Chen, Mu State Grid Smart Grid Research Institute Co. Ltd. Nanjing210003 China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China

ISBN: (纸本)9789819751808

The main existing network security problem of the current power marketing terminal is that the abnormal business behavior of the legitimate power marketing terminal cannot be perceived, and there is a risk of using it as a springboard to attack the power intranet caused by "one authentication, always trust". Therefore, this paper aims at the lack of "one-time authentication, always trust" behavior monitoring and evaluation technology of legal terminals in the existing terminal protection architecture, and studies the terminal anomaly discovery technology based on the degree of business behavior deviation. First of all, with the goal of real-time network traffic compression and formalized representation of behavior timing patterns of business terminals, a business timing symbolization method based on multivariate Gaussian mixture distribution is studied to simplify and symbolize the terminal behavior timing. Then, the terminal behavior symbolized data is reconstructed in a multi-dimensional standard format using the Gramian Angular Field. Finally, a terminal anomaly evaluation model of behavior deviation degree is proposed, and the detection result of the terminal is obtained through the calculation of behavior deviation to reflect the abnormality and credibility of the terminal. As a result, real-time monitoring and abnormal behavior identification of legitimate terminals being exploited are solved. The experimental results show that the method proposed in this article can accurately describe the characteristics of the original time series, accurately detect abnormal terminal behavior, and meet performance requirements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gaussian distribution

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