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APT Attack Investigation via Fine-grained Sequence Construction and Learning

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

引用

Computer technology and Information Science (ISCTIS), International Symposium on

作者： Tianqi Wu Zhuo Lv Daojuan Zhang Kexiang Qian Ming Wang State Grid Smart Grid Research Institute co. Ltd SGRI Power Grid Digitizing Technology Department State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Henan Electric Power Company State Grid Henan Electric Power Research Institute State Grid Laboratory of Power Monitoring System Cyber Security Technical Standard Verification School of Information and Communication Engineering Beijing University of Posts and Telecommunications State Grid Corporation of China co. Ltd Beijing China

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.

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Detecting Anomalous Behaviours Based on System Logs: A Dynamic Graph Perspective

Detecting Anomalous Behaviours Based on System Logs: A Dynam...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Ming Wang Yuman Wang Tianqi Wu Cen Chen Kexiang Qian State Grid Corporation of China co. Ltd Beijing China State Grid Smart Grid Research Institute co. Ltd SGRI Power Grid Digitizing Technology Department State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Henan Electric Power Company State Grid Henan Electric Power Research Institute State Grid Laboratory of Power Monitoring System Cyber Security Technical Standard Verification School of Information and Communication Engineering Beijing University of Posts and Telecommunications

System logs provide rich information about the current system’s state. To make use of system logs, various existing work extracts a causal graph from the log events, and then applies graph-based machine learning algorithms to detect the anomalous behaviors. One limitation of these work is that they treat the graph in a static way, neglecting the dynamic nature of anomalous behaviors. In this paper, we propose to detect anomalous behaviors based on system logs using dynamic graph modeling. Our method consists of several key steps, including negative sampling which helps to balance the dataset, edge encoding which encodes the spatial information for each behavior, and sequence modeling which further models the temporal information. Experimental evaluations on real datasets demonstrate the effectiveness of the proposed approach.

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Research and Application of Distribution Network Planning Based on Digital Twinning technology

Research and Application of Distribution Network Planning Ba...

引用

Decision Science & Management (ICDSM), International Conference on

作者： Junfeng Qiao Lin Peng Aihua Zhou Zhujian Ou Xiaoyi Xu Fuyun Zhu State Grid Laboratory of Power Cyber-Security Protection & Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing China Nantong Power Supply Company State Grid Jiangsu Electric Power Co. LTD Nanjing China

To satisfy the requirement of electric distribution grid planning brought by the forward annual electricity consumption growth and power grid expand, to determine where, when, and which type of substations, lines or distributed electric power generation device will be built. The future distribution network planning must adopt electric power flow, dynamic change of load and new distributed power generation in various directions, and build an electric distribution grid with grid structure. The results of distribution network planning has a huge impact on the operation efficiency of the power grid, the safety of electricity use, and the allocation of asset investment. This paper uses digital twinning, advanced computing and other technologies to divide and evaluate the scope of power supply, load characteristics, and user demand, and the scale of power facilities. With the power supply reliability demand as the core, it plans and builds a target network framework with high voltage quality, reasonable structure, flexible operation, and unified grid division standards, effectively giving play to the key role of planning in the construction and operation of distribution networks.

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Research and Implementation of Multi Fusion Data Model Construction technology for Distribution Network Digital Twins

Research and Implementation of Multi Fusion Data Model Const...

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Decision Science & Management (ICDSM), International Conference on

作者： Junfeng Qiao Lin Peng Aihua Zhou Zhujian Ou Yanfang Mao Sen Pan State Grid Laboratory of Power Cyber-Security Protection & Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing China Nantong Power Supply Company State Grid Jiangsu Electric Power Co. LTD Nanjing China

The core of digital twin is the digital reconstruction of physical space, that is the physical space perception and processed data are applied to the virtual space to achieve real-time mapping of physical space through full integration of data. With the rapid development of digital twin technology in the power field, in order to achieve the purpose of real-time data fusion of physical space and virtual space in the digital twin of distribution network, the virtual and real data fusion processing technology for digital twin has become the focus of research in the power field. In this paper, firstly, the characteristics and ontology technology of power digital twin virtual and real data are studied, and an ontology based virtual and real data fusion architecture is proposed. Then, a semantic feature extraction method is proposed for the structured and unstructured virtual and real data of power digital twin, and the extracted semantic features are formatted, described and stored. Then the overall architecture of the distribution network digital twin virtual real data fusion system is designed, the technical route of the system is analyzed and discussed, and the virtual real data fusion processing is completed using the similarity algorithm and fusion rules based on the elements between ontology. Finally, the functional modules of the distribution network digital twin virtual real data fusion system are divided and introduced, and the digital twin virtual real data fusion of the fan hub casting process is verified as an example.

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Risk Assessment of Distributed PV Microgrids Under cyber Attacks based on CNN-AE

Risk Assessment of Distributed PV Microgrids Under Cyber Att...

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IEEE International Conference on Smart grid and Smart Cities (ICSGSC)

作者： Shenjian Qiu Shiang Guo Zhipeng Shao Jiaxuan Fei Quan Liu Kaikai Pan State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD. Nanjing China Zhejiang University School of Electrical Engineering Hangzhou China State Grid Jiangsu Electric Power Co. Ltd. Yangzhou Power Supply Branch Yangzhou China

ISBN: (数字)9798350377408

ISBN: (纸本)9798350377415

The widespread application of smart grid technology and numerous Internet of Things (IoT) devices expose microgrid vulnerability to various cyber attacks. Moreover, the complex interactions and implicit risk propagation within the system pose challenges in accessing the risk from cyber attacks. To this end, we propose a risk assessment method for distributed PV microgrids under cyber attacks. This program employs an autoencoder (AE) architecture where convolutional neural networks (CNN) serve as the intermediate layer to extract the local features of data exchange from adjacent nodes. The AE conducts unsupervised learning using power data under normal operating conditions, thus performing risk assessment by evaluating the reconstruction errors. To validate the proposed method, we conducted training on the algorithm using historical data and the topology of a real scenario of a distributed photovoltaic microgrid and tested the risk assessment with various fake data injection attacks (FDIA) and Denial of Service (DoS) attacks.

关键词： Photovoltaic systems Distributed databases Microgrids Feature extraction Topology Smart grids Risk management Convolutional neural networks Internet of Things cyberattack

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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.

关键词： Drops

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Device Identification based on Network Traffic Fingerprint

Device Identification based on Network Traffic Fingerprint

引用

Artificial Intelligence and Intelligent Information Processing (AIIIP), International Conference on

作者： Tianqi Wu Xiaoming Zhou Hongbin Wu Daojuan Zhang Yingjie Zhang Xiao Han State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute co. Ltd Beijing China Operations Safety and Technology Department State Grid Liaoning Electric Power Supply Co. Ltd. Liaoning China

The devices connected to the LAN have the security risk of being counterfeited and remotely controlled. To identify and understand the devices in the network in advance, the administrator can monitor and handle the abnormal devices in advance. At present, the device recognition method based on deep learning has some limitations. In this paper, the fingerprint information related to the device type is extracted from the network traffic, and the deep learning algorithm based on graph neural network is used to deeply mine the traffic feature hiding relationship between devices, and effectively identify various device types and device models. The effect of the model in this paper on the task of device identification of public data sets has been greatly improved.

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Research and Implementation of Spatial Layout Algorithm and Optimization technology for Digital Twin Objects of Distribution Network Facilities

Research and Implementation of Spatial Layout Algorithm and ...

引用

International Conference on Applied Machine Learning (ICAML)

作者： Junfeng Qiao Lin Peng Aihua Zhou Zhujian Ou Xiaoyi Xu Jianguang Yao State Grid Laboratory of Power cyber-Security Protection & Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing Jiangsu China Nantong Power Supply Company State Grid Jiangsu Electric Power Co. LTD Nanjing Jiangsu China

The equipment and facilities in the distribution network include not only primary equipment such as transformers, circuit breakers, lightning arr-esters, capacitors, but also secondary equipment such as transfer switches, buttons, indicator lights, current and voltage meters. The total number of equipment types exceeds 200, and the average number of equipment in provincial areas exceeds 100 million. Equipment management is very arduous. The equipment management system in the power industry records the basic archive information of all these equipment, including equipment nameplate, equipment type, equipment operation mode, and geographic information. With the rapid development of power applications, the two-dimensional information description in device management systems cannot meet the needs of more complex power applications. The spatial description and layout of device objects is the foundation for the next step of power business applications. To address this issue, this paper proposes a layout and optimization algorithm for the digital twin object space of distribution network facilities, which visualizes distribution network equipment and facilities, and describes the three-dimensional features of the equipment using point cloud description methods, providing a spatial layout and optimization method for the equipment. The method proposed in this paper has been applied and verified in actual power production and maintenance business, improving the efficiency of power operation and maintenance work.

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Design of a Data Provenance Scheme with Virtual Tuple-Based Database Watermarking

Design of a Data Provenance Scheme with Virtual Tuple-Based ...

引用

Chinese Automation Congress (CAC)

作者： Qi Wang Aihua Zhou Yiqing Wang Zhen Zheng Jing Jiang Ruxia Yang State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute co. LTD Nanjing China State Grid XuZhou Power Supply Company Xuzhou China Qingpu Power Supply Company State Grid Shanghai Municipal Electric Power Company Shanghai China

The security of power grid is closely related to national security and social stability. With the development of smart grid and big data, grid data security has ushered in new challenges. The data provenance is one of the key issues. Considering the complexity of grid data storage types, this paper designs a virtual tuple-based data provenance scheme. This scheme firstly embedding the watermark with user identity through virtual tuple injection. Then, the data with watermark is distributed in the form of table splitting, which makes it possible to quickly locate the suspect user when a data leakage event occurs. Finally, the leaker is identified through watermark extraction and comparison. The simulation experiments show that our scheme has good robustness against insertion, replacement and deletion attacks.

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A comprehensive assessment method for the power structured data classification and grading based on hierarchical analysis

A comprehensive assessment method for the power structured d...

引用

International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Yujia Zhai Xiuli Huang Congcong Shi Shenglong Liu Hao Zhang State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. LTD Nanjing China Big Data Center of State Grid Corporation of China Beijing China State Grid Shandong Electric Power Company Jinan China

With the transformation of the digital development of power grids, the classification and grading of structured data for electricity is an important basis for data security protection. Existing methods for assessing data classification and grading results are mostly always based on individual indicators of the model's operational results. However, such methods can only evaluate a single performance indicator of that result, and cannot provide a comprehensive evaluation of all performance indicators. This paper proposes a comprehensive assessment method for the power structured data classification and grading based on hierarchical analysis, which allows various quality elements of the model to be extracted according to different needs, establishes a hierarchical indicator system, and determines the weights of each indicator in the system by means of hierarchical analysis, and finally calculates a comprehensive quality score for the classification and grading results. Finally, the method is systematically validated using different classification and grading models.

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