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检索条件"机构=State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology"

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

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

引用

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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User Identification based on Behavior Feature Analysis 7

User Identification based on Behavior Feature Analysis

引用

7th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2023

作者： Dai, Yuwen Zhang, Peng Wu, Tianqi Wu, Hongbin Zhang, Daojuan Lv, Zhuo Chen, Cen State Grid Smart Grid Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Beijing China State Grid Henan Electric Power Research Institute State Grid Laboratory of Power Monitoring System Cyber Security Technical Standard Verification Beijing China

ISBN: (纸本)9798350382877

The issue of identity management and information security in cyberspace is becoming increasingly important, and the issue of how to effectively identify through user behavior urgently needs to be addressed. At present, biometric recognition technologies have certain limitations. This article combines deep learning methods and uses the BERT model to extract input text information features to generate word vectors. It uses a bidirectional GRU network with local and global attention mechanisms for training, and finally uses a softmax classifier to classify the user identity recognition model. The model in this article has significantly improved its performance in user identity recognition tasks based on behavioral features. © 2023 IEEE.

关键词： Deep learning

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Abnormal Network Traffic Detection Algorithm Based on Improved Convolutional Neural Network

Abnormal Network Traffic Detection Algorithm Based on Improv...

引用

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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A 5G Slice Traffic Anomaly Detection Method Based on Convolution Neural Network 8

A 5G Slice Traffic Anomaly Detection Method Based on Convolu...

引用

8th International Conference on Signal and Image Processing, ICSIP 2023

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

ISBN: (纸本)9798350397932

As the network security threats caused by the Internet of Things (IoT) continue to increase, the attack surface continues to expand and the network heterogeneity increases, and the use of virtualization technology and distributed architecture increases. Protecting the future network will become a challenging field. This paper proposes the approach of converting 5G slice traffic into images for analysis by a CNN (Convolutional Neural Network) model, and uses NAS (Neural Architecture Search) to detect anomalous network traffic. In order to improve the accuracy and efficiency of the intrusion detection system, this paper uses SDS(Software Defined security) as a means to design a scalable, high-efficiency 5G traffic defense system. In the multiclass classification, the accuracy of CNN model is 94.9%. The results from this method are promising as the model has performed in multiclass classification with an accuracy of 96.4%. Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments. © 2023 IEEE.

关键词： Neural network models

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A Semi-Supervised Clustering-Based Approach for Sensing and Classifying Unknown Attacks in power Systems 7

A Semi-Supervised Clustering-Based Approach for Sensing and ...

引用

7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Li, Yong Shao, Zhipeng Zhang, Tao Ma, Yuanyuan State Grid Smart Grid Research Enstitute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection And Monitoring Technology Jiangsu Nanjing China

ISBN: (纸本)9798350359145

To solve the problem of perceiving and classifying unknown attacks in power systems, the study proposes a method for perceiving and classifying unknown attacks in power systems based on semi-supervised clustering. The method first uses a hybrid algorithm of improved genetic algorithm (GA) and improved multi-grain cascade forest (GC-forest) power network system unknown attack for perception and detection, then increases the number of training data by marking extension algorithm, provides specific information of detected attack or event using the idea of semi-supervised clustering, and finally filters the clustered unknown attack or event using similarity metric method. The analysis of the performance results testing showed that the perceptual detection of unknown attacks by the improved GC-Forest and the improved Genetic Algorithm (GA) had a check-all rate, precision and accuracy of 97.1% and 96.9%, 98.9% and 98.6%, 99.0% and 98.7%, respectively, and the average computing time was 3.12s and 3.68, respectively. for the classification of unknown attack types, the semi-supervised clustering-based (SSL) classification method has 100%, 95% and 95% classification accuracy for normal, non-power network attacks and power network attacks, respectively, while the adaptive classification method has 100%, 92%-98% and 90%-95%, respectively. Taken together, these data illustrate that the research can efficiently and accurately sense and classify unknown attacks on power systems and provide some reference value for fault sensing and classification in other areas. © 2023 IEEE.

关键词： Genetic algorithms

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Semi-supervised power Microservices Log Anomaly Detection Based on BiLSTM and BERT with Attention 2

Semi-supervised Power Microservices Log Anomaly Detection Ba...

引用

2nd International Conference on Advanced Electronics, Electrical and Green Energy, AEEGE 2023

作者： Zaojian, Dai Yong, Li Mu, Chen Liang, Chen Wengao, Fang Ziang, Lu State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co.Ltd. Nanjing China

ISBN: (纸本)9798350322866

The software industry has seen a rise in the size and intricacy of software in recent years, leading to a growing concern about how to accurately identify, analyze, and pinpoint abnormal behavior that may occur during system operation. power system microservices are becoming increasingly popular as a way to manage and monitor the complex and distributed attributes of modern power grids. System logs of power system microservices are used to document the status of power system and important occurrences at crucial points in order to help with troubleshooting performance problems and malfunctions, which plays a crucial role in maintaining the stability and reliability of power grids. Traditional anomaly detection methods based on white-box testing require access to the system source code and may not be applicable. Current abnormality detection methods based on system logs primarily focus on extracting log template characteristics, but they pay little attention to the semantic meaning of logs. In order to detect anomalies from system logs more effectively, this work proposes a log execution path anomaly detection method based on Bi-directional Long Short-Term Memory (BiLSTM) with attention. The semantics of log key is also taken into consideration using BERT model for semantic information extraction. Additionally, attention mechanism is used to adjust feature weights at different time steps and select important words. In training phase, a sliding window generates log key sequences from parsed logs to train the model. In detection phase, the model takes as input the parsed log sequence and outputs the probability distribution of the next log key. Experimental results indicate that the proposed method has outperformed conventional approaches on several metrics, including recall rate and F1 score. © 2023 IEEE.

关键词： Anomaly detection

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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...

引用

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...

引用

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...

引用

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...

引用

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