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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

来源：评论

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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

来源：评论

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Detecting Anomalous Behaviours Based on system Logs: A Dynamic Graph Perspective 8

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

引用

8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

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

ISBN: (纸本)9798350302455

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. © 2023 IEEE.

关键词： Graphic methods

来源：评论

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User Identification based on Behavior Feature Analysis

User Identification based on Behavior Feature Analysis

引用

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

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

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

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

引用

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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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

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