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FedTag: Towards Automated Attack Investigation Using Federated Learning 1

引用

1st International Artificial Intelligence Conference, IAIC 2023

作者： Chen, Mu Dai, Zaojian Li, Yong Lu, Ziang Department of Informatics and Communication Engineering Xiamen University Xiamen361005 China State Grid Smart Grid Research Institute Co. Ltd. Nanjing210003 China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing210003 China

ISBN: (数字)9789819712779

ISBN: (纸本)9789819712762

With the popularity of microservice systems in the industry, the security problems faced by microservice systems have received more and more attention, and the security of their systems is usually reflected in traces, logs, and some monitoring information, which record inter-service interactions and intra-service behaviors, respectively. Existing attack detection methods require a lot of manual intervention to label the data and have a large overhead in constructing the graph. In this paper, we propose a new federated learning-based attack investigation system, FedTag, which consists of three components: data preprocessing, model training, and attack investigation. The data preprocessing package fuses three kinds of log data from different sources, and the model training phase trains the BERT model in the form of joint learning to generate a pre-trained model, which is then fine-tuned to obtain a downstream classifier. In the attack investigation phase, FedTag first flags and embeds detected anomalous log queries then extracts events related to the attack investigation point and labels them as malicious events. Eventually, the attack story can be reconstructed by generating a causal graph or reporting all suspicious events to achieve traceability of attack links. Final experimental results show that FedTag displays TPR and FPR metrics comparable to AirTag when compared to the centralized model. Specifically, for S1–S6 single-host data, federated learning shows less than 1% drop and S2 achieves 100% TNR, outperforming AirTag for M1–M6 multi-host data, federated learning shows only about 1% drop. However, compared to centralized anomaly detection, FedTag not only detects log anomalies but also protects the privacy of each client’s log data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for power Micro-Application Server

A Web Attack Detection Method Based on DistilBERT and Featur...

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Advanced Electronics, Electrical and Green Energy (AEEGE), International Conference on

作者： Li Nige Chen Lu Zhao Lei Teng Zhenning Wang Zhiqiang Shao Yiyang Gui Xiaolin State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Nanjing China Shaanxi Province Key Laboratory of Computer Network Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

The widespread use of power micro-applications has significantly expanded the attack surface of their server side, thus increasing the risk of vulnerability attacks such as SQL injection, XSS, and CRLF injection. To ensure the security of power systems, these attacks must be detected precisely and timely. Therefore, this paper proposes a server-side web attack detection method based on DistilBERT and feature fusion for HTTP and HTTPS requests. The method treats HTTP and HTTPS requests as text data, specifically, extracts deep semantic features using DistilBERT, and fuses them with well-designed empirical features to comprehensively characterize and classify the request. Consequently, anomalous requests can be detected. The experimental results show that the accuracy, precision, recall, and F1 score of the method on HTTP CSIC 2010 and FWAF datasets are close to or higher than 99%. Compared with other methods as far as we known, this method has better performance and efficiency.

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Design of an Automated security protection System for power System

Design of an Automated Security Protection System for Power ...

引用

Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Jun Xia Jun Li Xinfan Jiang Bo Zhou Zesheng Xi State Grid Hunan Electric Power Co. Ltd Changsha China State Grid Hunan Electric Power Co. Ltd. Hunan Energy Big Data Intelligence Ltd Changsha China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. Ltd Nanjing China

In situations involving power system applications, a design plan for a new kind of automated security protection system has been put out. The self-repair vulnerability module, attack detection module, security policy configuration automation module, and front-end monitoring interface have all been designed for this article. This prototype system can secure device privacy and system stability while generating security policies automatically, responding to attacks quickly, and operating and maintaining expenses. This mostly entails the creation of self-repair and mining techniques for terminal vulnerabilities as well as detection techniques for malicious terminal-based assaults. In order to automatically create security protection device linkage methods, the automation module is configured to accept vulnerability and attack alert information. Each module opens its own database or offers interface forms for interactive operation and administration, while the front-end monitoring interface is created concurrently.

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Anomaly Detection Model for Data Interactions Based on Temporal Features in Electric power Data Centers

Anomaly Detection Model for Data Interactions Based on Tempo...

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Communication, Image and Signal Processing (CCISP), International Conference on

作者： Pengfei Yu Xiaoming Zhou Longdan Chen Jun Qi Liang Bai State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.LTD Nanjing China State Grid Liaoning Electric Power Supply Company Limited Shenyang Liaoning China Beijing University Of Posts and Telecommunications Beijing China State Grid Liaoning Electric Power Supply Co. LTD. Information & Telecommunication Branch Shenyang Liaoning China

The current power data centers face the risk of data leakage during the data exchange process. Given the increasing scale and complexity of power data centers, traditional methods for identifying anomalies in the temporal characteristics of data interactions are not applicable. However, there is currently no specific model available for identifying abnormal data interaction behavior in the context of power data centers. Directly using existing models would result in low efficiency and fail to meet the requirements of power data center systems. This article proposes a model, VAE-GAN, for identifying abnormal data interaction behavior with multivariate temporal characteristics specifically designed for power data center business scenarios. The Variational Auto Encoder (VAE) architecture allows the model to learn in an unsupervised manner. The use of Generative Adversarial Networks (GAN) and their architecture helps address the drawbacks of VAE networks in terms of smoothness and reconstruction ability in latent spaces, thereby improving the model's generation and reconstruction performance. Experiments were conducted on a dataset provided by a power company to study the effectiveness of the VAE-GAN method in power data center business scenarios. The results demonstrate its usability in the context of power data centers.

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Deep Integration and Learning of Measurement Data in Active Distribution power Networks for Load Flow Estimation Considering Incomplete Information

Deep Integration and Learning of Measurement Data in Active ...

引用

China International Electrical and Energy Conference (CIEEC)

作者： Ruisheng Diao Aihua Zhou Ruiyuan Zeng Anqi Wang Xiaofeng Shen Jingde Shun Hua Gu State Key Laboratory of Clean Energy Utilization ZJU-UIUC Institute Zhejiang University Haining Zhejiang China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing Jiangsu China ZJU-UIUC Institute Zhejiang University Haining China Qingpu Power Supply Company Stage Grid Shanghai Municipal Electric Power Company Shanghai China

To reach the goals of carbon peaking by 2030 and carbon neutrality by 2060 in China, a growing penetration of PV generation is integrated into the modern distribution power grid, causing challenges in operating the grid to meet security requirements due to the increased dynamics and stochastics. A deep neural network (DNN)-based method is proposed in this paper for estimating load flow solutions in active distribution power networks that typically suffer from incomplete measurements. Adaptive DNN models are trained from massive historical observations obtained from actual measurements or high-fidelity simulations, whose hyperparameters are tuned automatically. Comprehensive case studies are conducted on the IEEE 123-node feeder model with renewable generation considering various levels of missing information as inputs, which validate the effectiveness and robustness of the proposed method.

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An Intelligent Multi-Source Data Clustering and Analysis Method for AC/DC Hybrid power grid Planning with Increased Complexity

An Intelligent Multi-Source Data Clustering and Analysis Met...

引用

China International Electrical and Energy Conference (CIEEC)

作者： Tu Lan Aihua Zhou Ruisheng Diao Fangyuan Sun Ruiyuan Zeng Xiaofeng Shen Chenhong Huang Zhen Zheng ZJU-UIUC Institute Zhejiang University Haining Zhejiang China State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing Jiangsu China State Key Laboratory of Clean Energy Utilization ZJU-UIUC Institute Zhejiang University Haining Zhejiang China Qingpu Power Supply Company Stage Grid Shanghai Municipal Electric Power Company Shanghai China

In order to better analyze the response characteristics of an AC/DC hybrid power grid with increased complexity following various types of disturbances, this paper proposes an intelligent method for clustering dynamic responses of multi-source data obtained from actual measurements or high-fidelity simulators. Base cases for AC/DC hybrid power grid planning students are created first, and a list of high-impact contingencies is searched using a graph algorithm. Then, the electromechanical-electromagnetic hybrid simulation method is adopted to create multi-source simulation data in terms of dynamic voltage, frequency, etc. An unsupervised learning algorithm and cluster analysis are then conducted to extract key features of dynamic response characteristics of the AC/DC hybrid power grid and create specific cluster labels. Advanced analysis, e.g., fast evaluation of risks from multi-source steady-state data, can then be enabled for further analysis. The proposed approach's effectiveness is verified on the actual AC/DC power grid model in East China.

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The application of voltage direct detection method based on mathematical morphology in distribution network with DG

The application of voltage direct detection method based on ...

引用

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Peng Li Wei Wang Hongfen Cui Shuai Wang Xilei Yang Yishu Zhao Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control under Ministry of Education North China Electric Power University Baoding Hebei China State Grid of China Technology College Jinan Shandong China

Distributed generation (DG) injects new vigor into the power system, but it sometimes brings unfavorable factors influencing the power quality of distribution network. In this paper, the Dynamic Voltage Restorer (DVR) is introduced to the distribution network to improve the power quality. In view of that the detection of voltage compensation is the key factor that influences the effect of voltage compensation, a voltage direct detection method based on mathematical morphology is proposed. Firstly, the positive-sequence voltage with harmonics can be extracted by the instantaneous sequence-component decomposer. And then the harmonics is filtered by mathematical morphology filter. Lastly, the control variable of voltage compensation needed by DVR can be calculated from the voltage direct-detecting algorithm. The simulation results show that this method has the characteristics of favorable accuracy, real-time performance and simplicity, and assures the voltage quality of load side after compensation by DVR.

关键词： Morphology power quality power system harmonics power harmonic filters Active filters Voltage fluctuations power system dynamics Voltage control power generation power system reliability

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Research on Virtual Resource Scheduling Algorithm Based on Multi-Objective Optimization

Research on Virtual Resource Scheduling Algorithm Based on M...

引用

cyber Science and technology Congress (cyberSciTech), IEEE

作者： Cheng Zhou Xiaojuan Guan Chen Wang Wen Ji Chenhan Lin State Grid Laboratory of Power cyberSecurity Protection and Monitoring Technology China Electric Power Research Institute Co. Ltd Nanjing China State Grid Fujian Information & Telecommunication Company State Grid Fujian Electric Power Co.Ltd Fujian China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

In order to solve the resource management challenge of cloud service center when the user demand peaks periodically, reduce resource waste and energy consumption, improve resource utilization and system performance, a scheduling algorithm based on multi-objective optimization is proposed in this paper. Firstly, this paper analyzes the main problems of current cloud computing resource scheduling, constructs an adaptive virtual resource scheduling framework, establishes a multi-objective scheduling optimization model, and ensures the balance of resource allocation by quantifying task levels and establishing a “task-virtual node” allocation model. To solve the model, a load-balancing strategy based on improved particle swarm optimization (PSO) was proposed to improve resource utilization and reduce energy consumption. Other scheduling factors that need to be considered in the actual scheduling environment, including computing cost, load, task completion time, and convergence speed, are further studied and improved in the experiment. Based on the above scheduling factors, multi-objective optimization of cloud computing resource scheduling is carried out to achieve the optimal comprehensive performance of cloud computing task scheduling. Finally, through simulation experiments, it is verified that the algorithm has better performance in terms of cost, load, task completion time, convergence speed, and CPU consumption, especially when the task volume increases; it can balance the load and reduce energy consumption more effectively.

关键词： Cloud computing Energy consumption Costs Scheduling algorithms Computational modeling Resource management Particle swarm optimization Optimization Convergence Load modeling

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Research on the Application of Large Language Models in Shore power Management Decision 4

Research on the Application of Large Language Models in Shor...

引用

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

作者： Fu, Rong Jiao, Yannan Chen, Dalei Lan, Zhengyin Hu, Weimin Qu, Jie China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering 443002 China Yichang Power Supply Company of State Grid Hubei Electric Power Co. Ltd. Yichang443002 China China Three Gorges University College of Computer and Information Technology Yichang443002 China Yichang Power Supply Company of State Grid Hubei Electric Power Co. Ltd. Yichang443000 China The Third Research Institute The Ministry of Public Security Shanghai China

ISBN: (纸本)9798331532765

To ensure both quality and efficiency of the shore power operation and maintenance system, the value of big data of shore power operation and maintenance can be continuously exploited by the digital transformation operation with the state of art large language model technology. This paper discusses the two main tasks in shore power management decision, namely the construction of shore power operation and maintenance knowledge base and the in-depth mining of shore power operation. It analyzes the technical features and applications of the typical models of large model technology. Finally, it discusses the prospect of large model application in the power industry shore power management decision application and proposes a management framework for large model application. © 2024 IEEE.

关键词： data mining Generative Pre-trained Transformer large model shore power management

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A Searchable power Engineering Design Document Distribution Model based on Alliance Blockchain

A Searchable Power Engineering Design Document Distribution ...

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power and Energy (PandaFPE), Panda Forum on

作者： Jia-Yuan Fan Xian-zhong Li Qin Gong Jun Zhao Information Dispatching and Monitoring Center State Grid Sichuan Province Power Company Information & Communication Company Chengdu China Marketing Department State Grid Sichuan Province Power Company Chengdu China Inspection Department State Grid Sichuan Province Power Company Marketing Service Center Chengdu China Cyber security College Chengdu University of Information Technology Chengdu China

The design document is an important basis for the establishment, construction and production of power engineering construction. After the power engineering design documents are completed by the design unit, they are usually distributed to other units in the form of printing paper documents and making blueprints. The current method of document distribution has resulted in higher costs of document printing and storage, and the efficiency of document distribution is low. In this paper, a searchable power engineering design file distribution model based on the Alliance chain is stated. It uses the main feature of the blockchain's data tamper-proof technology, and combines the searchable encryption technology to realize the distribution and access management of digital power design files, eliminating the problems of inconsistent file versions and malicious tampering of file contents in the existing design file distribution process. In this model, the feature information ciphertext and keyword index ciphertext of the design file are written into the block data of private chain of design unit, and private chain send transaction to alliance blockchain. At the same time, the design documents are stored in distributed file systems, and the feature information of the documents is compared during the access of the design documents, so that the design documents can be tamper-proof after distribution, and the solution of replacing the paper blueprints with digital design drawings is realized. The model can make the engineering documents have the ability of tamper-proof, and can trace the flow process of the documents.

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