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Towards practical anonymous quantum communication: A measurement-device-independent approach

Physical Review A 2021年第5期104卷 052415-052415页

作者： Yu-Guang Yang Xiao-Xiao Liu Shang Gao Yi-Hua Zhou Wei-Min Shi Jian Li Dan Li Faculty of Information Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing Beijing 100124 China College of Network Security Beijing University of Posts and Telecommunications Beijing 100876 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

The multipartite Greenberger-Horne-Zeilinger (GHZ) entangled state is an important resource for anonymous quantum communication tasks. But its low intensity and fragility limit the practical applications of these anonymous communication tasks under current conditions. Here, we present a feasible anonymous communication protocol in the measurement-device-independent framework. We analyze its performance in a quantum network under certain types of noise. Then we compare the performance of our protocol with the GHZ-state-based anonymous protocol. We demonstrate that our protocol has a unique advantage, i.e., it can tolerate an arbitrary number of nonresponsive nodes. We prove that the security of our protocol in an active adversary scenario in which active malicious agents and an untrusted ancillary node are taken into account. Utilizing the measurement-device-independent technology, our proposal suggests an important avenue for practical anonymous quantum communication.

关键词： Quantum communication Quantum cryptography

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security Vulnerability Assessment of power IoT based on Business security

Security Vulnerability Assessment of Power IoT based on Busi...

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作者： Jiaxuan Fei Kai Chen Qigui Yao Qian Guo Xiangqun Wang State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute Co.Ltd. State Grid Hebei Information &Telecommunication Branch State Grid Hebei Electric Power Co.Ltd.

power Internet of Things is the application of IoT in smart power *** attacked,it will cause huge ***,it is necessary to conduct a security assessment to take defensive ***,the traditional vulnerability assessment methods of the power Internet of things mostly focus on the security of the system itself,without considering the impact on business economy and *** paper proposes a security vulnerability assessment method of power Internet of Things integrating business *** method first analyzes the security risks faced by the power Internet of Things,and establishes its attack tree ***,each leaf node is rated from the three safety features,which are weighted by evaluation and calculation,and the activation probability of each leaf node is *** that,considering the blind attack factor,the activation probability of all nodes in the model is ***,the vulnerability of the system and the vulnerability sensitivity of each leaf node are *** to the vulnerability sensitivity,measures are taken to protect the weak links of the *** effectiveness of the proposed method is verified by experiments on SCADA(supervisory control and data acquisition)system in the power Internet of things.

关键词： power Internet of Things vulnerability assessment attack tree model SCADA system

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A power IoT Terminal Asset Identification Technology Suitable for Modbus Protocol

A Power IoT Terminal Asset Identification Technology Suitabl...

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Interactive Intelligent Systems and Techniques (IIST), International Conference on

作者： Yong Li YuanYuan Ma Mu Chen Chao Wang State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Enstitute Co. Ltd Nanjing China State Grid Beijing Electric Power Research Institute Beijing China

ISBN: (数字)9798350374421

ISBN: (纸本)9798350374438

In the new power system scenario, power IoT terminals have new characteristics such as diverse hardware heterogeneity and complex subject ownership, which make it technically difficult to achieve secure access authentication based on traditional terminal hardware fingerprint recognition technology. A power IoT terminal fingerprint recognition technology suitable for the Modbus protocol is proposed to solve this problem. This method combines the characteristics of Modbus protocol packet parsing, which is a common industrial control communication protocol. Firstly, based on active packet detection technology, it realizes the survival judgment of business terminals and constructs a coarse-grained topology image of terminal production. Then, by passively monitoring and analyzing the characteristics of terminal interaction traffic behavior, a terminal behavior fingerprint recognition model is constructed based on the deep learning algorithm BiLSTM to achieve accurate terminal type discrimination, thereby establishing a power IoT terminal fingerprint recognition model. The experimental results in a distributed photovoltaic scenario show that the method has an average classification and recognition accuracy of 89.6% for common power business terminals, which can basically meet the lightweight and non-invasive business requirements of terminal fingerprint recognition in new power system scenarios.

关键词： Photovoltaic systems Analytical models Protocols Accuracy Fingerprint recognition power systems Topology Monitoring Business Load modeling

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An Abnormal Login Detection Method Based on Local Outlier Factor and Gaussian Mixture Model

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International Journal of network security 2023年第2期25卷 297-305页

作者： Guo, Wei He, Yue Chen, He-Xiong Hang, Fei-Lu Li, Yun-Jie Information Center of Yunnan Power Grid Company Limited Yunnan Kunming650034 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China

Nowadays, various enterprises have gradually begun to provide Internet services for customers. And the security of customer accounts is constantly threatened by various factors, which may result in account password disclosure and theft. Therefore, it is an important task to identify and screen users’ abnormal login behavior to protect the customers’ interests and the servers’ security. At the same time, it becomes a key security problem to extract important features from the server’s user login data and detect abnormal login behaviors. This paper proposes a method that extracts features from the login log. It reduces the dimensions of data, then calculates the outlier score of time data and IP address data after dimension reduction by adopting the local outlier factor algorithm and Gaussian mixture model, and comprehensively analyzes outlier scores. The experimental results show that this method performs high accuracy, precision ratios, and so on, around 95%, which means it can correctly calculate the corresponding outlier score according to the abnormal degree of login behavior © 2023, International Journal of network *** Rights Reserved.

关键词： Gaussian distribution

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BrInstFlip: A Universal Tool for Attacking DNN-Based power Line Fault Detection Models

BrInstFlip: A Universal Tool for Attacking DNN-Based Power L...

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IEEE International Conference on Communications in China (ICCC)

作者： Yixin Jiang Yanbo Xu Zhihong Liang Wenqian Xu Tian Dong Ximing Zhang Yiwei Yang Yan Meng Haojin Zhu Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China China Southern Power Grid Guangzhou China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Deep learning neural network (DNN) models are currently experiencing significant success in domains like image classification. In the realm of power grids, there have been numerous attempts to leverage DNN models for fault diagnosis in power grid systems. However, they are also susceptible to various attacks, such as adversarial samples, model poisoning, backdoors, and other attack vectors. These existing attacks exhibit limitations to varying degrees, hampering their ability to execute generalized attacks across models. Designing a universal attack strategy for different DNN models poses challenges. In this paper, we present a novel attack strategy centered around runtime code fault injection, aiming to disrupt the inference capability of DNN architectural fault detection models in smart grid. Our approach meticulously identifies vulnerable bits within common code segments, such as underlying libraries, susceptible to functional bias through automated searches. Leveraging the DRAM Rowhammer Bug, we manipulate memory bits, inducing bias in the functionality of the affected library, consequently impacting all higher-level machine learning frameworks utilizing it. We conducted tests on the VGG-16, ResNet34, and ResNet50 models for the Vari-grip fault detection task using the InsPLAD dataset. Before the attack, the models achieved accuracies of 86.62%, 92.88%, and 97.12% on the test set, respectively. However, after the attack, their accuracies dropped to 0%, which can lead to severe consequences like failure of power grid repair. These results underscore the effectiveness and generalizability of our attack strategy.

关键词： Accuracy Codes Runtime Fault detection Neural networks Random access memory Libraries

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FP-OCS: A Fingerprint Based Ownership Detection System for Insulator Fault Detection Model

FP-OCS: A Fingerprint Based Ownership Detection System for I...

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IEEE International Conference on Communications in China (ICCC)

作者： Wenqian Xu Fazhong Liu Ximing Zhang Yixin Jiang Tian Dong Zhihong Liang Yiwei Yang Yan Meng Haojin Zhu China Southern Power Grid Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China China Southern Power Grid Guangzhou China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

In smart grids, the robustness and reliability of the transmission system depend on the operational integrity of the insulators. The success of deep learning has facilitated the development of advanced fault detection algorithms for classifying and identifying insulator states. However, these machine learning based detection systems rely on high-quality datasets, making them potential targets for intellectual property theft, and model extraction attacks pose risks of privacy breaches and unauthorized exploitation. To address the challenge of protecting neural network ownership in this situation, we introduce a fingerprint based ownership detection system for insulator fault detection model FP-OCS. FP-OCS uses model extraction attack to generate a series of piracy models, and uses white-box access victim models to generate similarity models and universal adversarial perturbation. The system’s fingerprint generation module augments the original dataset to craft distinctive model fingerprints. Subsequently, FP-OCS’s encoder training module extends the fingerprint dataset using K-means methods and uses contrast learning to train the encoder network and the projection network. Upon finalization of training, FP-OCS evaluates a model’s authenticity by matching its derived fingerprint against the victim model. We evaluated the effectiveness of the system using data-enhanced InsPLAD datasets. Our findings prove that FP-OCS can achieve 100% accuracy in Ownership Detection tasks with 50% similarity dividing line.

关键词： Training Fault detection Perturbation methods Fingerprint recognition Insulators Privacy breach Robustness

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Multi-Classification Segmentation Method of Gastric Cancer Pathological Images Based on Deep Learning

Multi-Classification Segmentation Method of Gastric Cancer P...

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Advanced information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

作者： Hehu Zhou Jingshan Pan Li Na Qingyan Ding Chengjun Zhou Wantong Du Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Pathology The Second Hospital of Shandong University Jinan China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Gastric cancer is a serious health threat, and pathological imaging is important in detecting it. These images can assist doctors in accurately determining the location of the cancer, thereby providing an important reference for clinical decision-making. In the field of image processing, deep learning technology is leading to more and more excellent segmentation models. The Trans-Unet model has achieved success in the image segmentation. However, when applying this model to gastric cancer pathological section data, the segmentation boundary appears jagged. We propose three possible solutions. First, we designed an attention connection module to replace the skip connections in the model to enhance the prediction accuracy of the model. Second, we designed a prediction processing unit that takes the model’s prediction results as input and uses a Conditional Random Field (CRF) for further prediction calculations. To enable the model to meet the requirements of multi-classification of images, we designed a dynamic weight selection module to enhance the model’s accuracy for multi-classification tasks. After our optimization, the optimized model improved by 8% on the DSC evaluation index and 39% on the HD evaluation index. In addition, the jagged boundary problem in the prediction results has also been effectively improved. Through comparative experiments and erosion experiments, we found that the improved method enhances the accuracy of model prediction and reduces the jagged results at the boundary.

关键词： Training Image segmentation Pathology Adaptation models Accuracy Computational modeling Predictive models Data models Conditional random fields Cancer

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Fusion of Dynamic Hypergraph and Clinical Event for Sequential Diagnosis Prediction

Fusion of Dynamic Hypergraph and Clinical Event for Sequenti...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xin Zhang Xueping Peng Hongjiao Guan Long Zhao Xinxiao Qiao Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

Sequential diagnosis prediction (SDP) is a challenging task, aiming to predict patients’ future diagnoses based on their historical medical records. While methods based on graph neural networks (GNNs) have proven successful for this task, they typically focus on modeling pairwise diseases using a global disease combination graph. However, these approaches neglect the fine-grained higher-order relations among persistent and emerging diseases within a single visit, which may contain crucial clues to predict the next diagnosis. Additionally, they fail to fully leverage patient-related clinical information present in electronic health records (EHRs). To address these challenges, this paper proposes a novel approach called the fusion of Dynamic Hypergraph and Clinical Event (DHCE) for sequential diagnosis prediction. The proposed method aims to exploit the fine-grained higher-order relations among diagnoses within a visit and leverage clinical event information from EHRs to improve the accuracy of predicting the next diagnosis. Specifically, DHCE categorizes diagnoses within a single visit in a fine-grained granularity into persistent and emerging categories based on a patient’s historical diagnoses. It then constructs dynamic hypergraphs to capture higher-order disease relations within each visit. Next, we design a transition function to extract the transitional context from previous visits in order to generate the visit representation. Furthermore, to fully leverage patient-related clinical events in a visit, we utilize Bio-Clinical BERT to encode them and generate the clinical event representation for each visit. Finally, we combine the visit representation and event representation to generate a comprehensive patient representation, which is then used to predict the patient’s next diagnosis. Experimental results on two benchmark datasets consistently demonstrate that DHCE outperforms state-of-the-art methods 1 .

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Wireless powered Backscatter Communications with Spatially Random Tags

Wireless Powered Backscatter Communications with Spatially R...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Yingting Ye, Yinghui Li, Xingwang Ma, Zhongyu Zhou, Zhiyang Lanzhou Jiaotong University School of Electronic and Information Engineering Lanzhou730070 China State Grid Gansu Electric Power Research Institute Lanzhou730070 China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China Xi'an University of Posts & Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China College of Computer Science and Engineering Northwest Normal University Lanzhou730070 China

ISBN: (纸本)9798350345384

In this paper, a wireless powered backscatter communication (BackCom) system is considered, which consists of one dedicated radio-frequency source node (S), one destination node and multiple tags randomly located in the coverage region of S. The consumed power by tags is considered and an optimal dynamic reflection coefficient (RC) is used to maximize the power of the backscattered signals while just satisfying the tag's power requirement. The random tag selection (RTS) and nearest tag selection (NTS) schemes are proposed and the corresponding outage probabilities and diversity orders are derived. Numerical results verify the theoretical analysis and show that the NTS scheme has the best performance compared to the RTS and farthest tag selection schemes. Moreover, numerical results also show that the performance of the adopted optimal dynamic RC is superior to that of the fixed RC. © 2023 IEEE.

关键词： Backscattering

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Research on Indoor and Outdoor Comprehensive Positioning Technology Based on Multi-Source information Assistance

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Procedia Computer Science 2020年 166卷 361-365页

作者： Zhi Min He Lin Peng Hai Yun Han Hao Lu Zhi Feng Wang Peng Zhao Global Energy Interconnection Research Institute Co. Ltd Nanri Road 8 Nanjing 210003 China State Grid Key Laboratory of Information & Network Security Beiqijia Beijing 100000 China State Grid Shaanxi Electric Power Company Information and Communication Corporation Xi’an 710048 China

:After investigation and analysis, this study involves a multi-source information-assisted indoor and outdoor integrated positioning system and method based on point cloud identification and power equipment identification. It uses GPS and other auxiliary information to combine point cloud identification and intelligent identification of power equipment. Comprehensive positioning method and system to achieve fast decimeter-level precision positioning in indoor and outdoor environments.

关键词： Multi-source information Indoor outdoor positioning Point cloud recognition

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