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2022 International Conference on Electrical, Control and information Technology, ECITech 2022

作者： Wang, Qi Peng, Jianing Zheng, Tiejun Yong, Shaohua Zhang, Xiaojian Global Energy Interconnection Research Institute Co. Ltd Jiangsu Nanjing210003 China State Grid Key Laboratory of Information & Network Security Jiangsu Nanjing210003 China State Grid Ningxia Electric Power Co. Ltd Ningxia Yinchuan750001 China State Grid Zhongwei Electric Power Supply Company Ningxia Zhongwei755001 China

ISBN: (纸本)9783800759170

Passwords play an important role in power system security protection. But the existence of weak passwords poses a great threat to the security of the power system. However, the current password management methods of power industrial control systems have loopholes in which some weak passwords are not detected. This paper reference the design method of the PCFG algorithm and proposes a weak password scanning scheme. Hot words and personal information are introduced into the construction of a weak password dictionary for the first time, and the weak password dictionary is expanded based on machine learning technology. Passwords are stored as ciphertext in the power system. According to this characteristic, this scheme calculates the hash value of the weak password dictionary and compares that stored in the target system. This scheme can scan weak password ciphertext and is more suitable for the power system. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Authentication

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Research on Automated Penetration Testing Framework for power Web System Integrating Property information and Expert Experience 9th

Research on Automated Penetration Testing Framework for Powe...

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9th International Conference on Computer Engineering and networks, CENet2019

作者： Xi, Zesheng Cui, Jie Zhang, Bo Institute of Information and Communication Global Energy Interconnection Research Institute NanjingJiangsu China State Grid Key Laboratory of Information & Network Security NanjingJiangsu China State Grid Tianjin Electric Power Research Institute Tianjin China

ISBN: (纸本)9789811537523

Faced with the heterogeneous, intelligent and interconnected massive power Web system environment, it is difficult to meet the needs of existing and incremental services in efficiency by mining and verifying vulnerabilities manually. Therefore, an automated penetration testing framework for power Web system is proposed. Integrating property information and expert experience, the framework guides vulnerability mining path and vulnerability exploiting method selection, and realizing automatic vulnerability verification and exploitation of Web system through autonomous decision module. The experimental results show that the framework can simulate real attack behavior and efficiently verify and exploit common Web system vulnerabilities. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Websites

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Research on the Distribution Characteristics of Standardized Big Data Resources Based on Data Visualization——Taking Standardization Practice in Shandong Province as an Example

Research on the Distribution Characteristics of Standardized...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Jinyang Sun Dongdong Peng Zhe Lu Mengran Zhai Ning Qi Yuanhua Qi SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Jinan Municipal Bureau of Big Data Jinan China Shandong Institute of Economy and Informatization Development Jinan China 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 Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Standardized big data, as a product of the combination of big data and standardization work, objectively reflects the status quo and trends of standardization work. An in-depth study of the distribution characteristics of standardized big data resources has important value for improving the efficiency of standardization. Therefore, this paper explores the characteristics of the distribution of standardization resources from the dimensions of spatial and temporal distribution, industry distribution, standard type distribution, and distribution of think tank experts through visual analysis methods. The study shows that: (1) the distribution of standardization resources in Shandong Province presents significant hierarchical and geographical characteristics; (2) from the viewpoint of the distribution of industry types and standard types, the distribution of standardization resources in Shandong Province presents a typical "long-tailed distribution"; (3) the distribution of think-tank experts presents obvious "agglomeration effect".

关键词： Industries Technological innovation Heavily-tailed distribution Animals Urban areas Data visualization Forestry Big Data Fisheries Standards

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Topic-based Multi-layer Knowledge Filtering for Emotion Recognition in Conversation

Topic-based Multi-layer Knowledge Filtering for Emotion Reco...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiaoyang Li Zhenyu Yang Yiwen Li Zhijun Li 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Emotion recognition in conversation (ERC) is a prominent research topic in natural language processing, widely applicable in various scenarios. However, accessing external commonsense knowledge and related dialogue topics, along with the complexity of fusing utterances, presents significant challenges. In this paper, we introduce a Topic-based Multilayer Knowledge Filtering (TMKF) model to enhance dialog emotion recognition accuracy. TMKF acquires commonsense knowledge for each utterance and employs two Variational Autoencoder (VAE) modules to extract global and speaker-level dialogue topics. We utilize Global Knowledge Filtering (GKF) and Local Knowledge Filtering (LKF) to obtain topic-specific knowledge representations after filtering commonsense information through global and local topic hierarchies. Subsequently, we leverage relational graphs to convolve commonsense knowledge with utterances, resulting in knowledge-constrained utterance representations for classification. Our proposed model is evaluated through extensive experiments on four widely used benchmark datasets for conversational emotion recognition. The results underscore the effectiveness of TMKF, which significantly outperforms other methods in standard metric evaluations.

关键词： Measurement Emotion recognition Filtering Convolution Oral communication Benchmark testing Nonhomogeneous media

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Research on Optimization and Scheduling of MPTCP Data networks in GNSS network Reference Stations

Research on Optimization and Scheduling of MPTCP Data Networ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Yuxin Wang Hoiio Kong Dexin Ma Xiyuan Zhao 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) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Global Navigation Satellite System network Reference Stations (GNSS-NRS) are pivotal in modern po-sitioning and navigation applications. However, GNSS-NRS face significant communication challenges, including instability during rapid user movement and inconsistent coverage by different operators, which often results in data transmission interruptions and delays. Moreover, the conventional Multipath Transmission Control Protocol (MPTCP) scheduling strategy does not accommodate the diversity of the data types managed by GNSS-NRS. This default approach treats all data uniformly, even under deteriorating network conditions. To overcome these limitations, this paper introduces a ‘Multiple Reception’ strategy based on MPTCP that ensures timely and reliable GNSS data transmission. This novel strategy differentiates between observational and control data based on their real-time criticality and applies tailored transmission techniques. Under adverse conditions, it utilizes multiple transmission paths and selectively receives control data, thus prioritizing the transfer of critical information. We conducted simulation experiments using MiniNet to compare the ‘Multiple Reception’ method against conventional and redundant strategies, demonstrating that it significantly reduces data loss rates in GNSS-NRS.

关键词： Global navigation satellite system Protocols Real-time systems Delays MPTCP Reliability Data communication Kernel Optimization Faces

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Remote Attestation for Intelligent Electronic Devices in Smart Grid Based on Trusted Level Measurement

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Chinese Journal of Electronics 2020年第3期29卷 437-446页

作者： WANG Yong LI Jun'e CHEN Xiong LIN Hai YU Fajiang LUO Jianbo Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and Engineering Wuhan University NARI Group Corporation State Grid Electric Power Research Institute NARI Technology Co.Ltd. State Key Laboratory of Smart Grid Protection and Control

To reduce the implementation complexity and excessive overhead caused by remote attestation,we propose a new remote attestation scheme based on trusted level measurement considering the limited system resources of the Intelligent electronic devices(IEDs) in smart grid. In this scheme, a lightweight trusted level measurement mechanism is designed, and a new dynamic weighted multi-classifier integration method based on Binary tree Support vector machine(BT-SVM) is proposed. The trusted level of configuration information and running state of an IED are measured through trusted third party. The measurement parameters and attestation period are dynamically adjusted according to different trusted levels of an IED, so that reducing computational overhead and communication delays in remote attestation process. Analysis and experiments show that the proposed scheme can enhance the security of IEDs in smart grid by occupying a small amount of computation and communication resources.

关键词： Smart grid Intelligent electronic devices(IEDs) Remote attestation Trusted level measurement

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TSFE: Research on Transformer and Feature Enhancement Methods for Image-Text Retrieval

TSFE: Research on Transformer and Feature Enhancement Method...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jinxiao Yang Xingang Wang Qingao Wang Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, cross-modal retrieval has a wide range of applications in fields such as information retrieval and multimedia processing, where the key is to find relevant data in another modality through one modal query. In cross-modal graphic retrieval, mutual attention mechanisms are commonly used to interact image and text features. However, this interaction mechanism makes it difficult to extract separate visual and textual features for subsequent indexing steps, especially in large-scale retrieval tasks. Furthermore, in image representation, non-semantic information in image features can lead to inaccuracies in the alignment of image and text features. Therefore this paper proposes a Transformer and Feature Enhancement Method (TSFE) for Image-Text Retrieval. The method better fuses the underlying features and the higher level features by using a hierarchical Transformer coding structure on two separate branches, image and text, to proofread and localise the final output features. The Feature Enhanced Attention (FEA) module is designed in order to infer non-semantic information in the image and enhance the features of salient regions. Channel-space attention and max pooling is introduced on top of Transformer encoder self-attention to filter redundant and interfering information from channel and spatial dimensions. Experiments on Flickr30k dataset and MS-COCO dataset in this paper show improved performance in cross-modal retrieval, thus validating the effectiveness and sophistication of the proposed method.

关键词： Visualization Semantics Pipelines Noise Image representation Transformers Feature extraction

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

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security and Safety 2022年第1期1卷 121-141页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua University Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer Science Tongji University Shanghai Network Financial Security Collaborative Innovation Center Tongji University

Nowadays, large numbers of smart sensors(e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services(DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning,we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer(D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intelligent transportation systems

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Adaptive Interacting Multiple Model for Underwater Maneuvering Target Tracking with One-Step Randomly Delayed Measurements

SSRN

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SSRN 2023年

作者： Li, Xiaohua Lu, Bo Li, Yuxing Lu, Xiaofeng Jin, Haiyan School of Computer Science and Engineering Xi'an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China School of Automation and Information Engineering Xi'an University of Technology Xi’an710048 China

The existing interacting multiple model (IMM) suffers from two problems when tracking an underwater maneuvering target: 1) the conventional nonlinear filters in IMM cannot work on the condition of delayed measurement, 2) the inefficiency of model switching in IMM may degrade the tracking performance. In order to handle the delayed measurement, an augmented state vector including target state and measurement noise is designed. Thus, under two Gaussian assumptions, the estimation of the target state can be divided into two steps: measurement noise filter and target state filter. Then, the new extended Klaman filter (EKF) and unscented Kalman filter (UKF) with delayed measurement are implemented as two variations of the proposed filter. In addition, to solve the problem of low switching efficiency between IMM models, a transfer probability corrected function is designed to adaptively update the transfer probability matrix (TPM) in IMM. The improved TPM can make full use of the current and past model information. The simulation results show that the proposed algorithm outperforms the existing IMM-EKF and IMM-UKF. © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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A Prediction Method for Atmospheric Monitoring Data Based on Transformer and GAN

A Prediction Method for Atmospheric Monitoring Data Based on...

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IEEE information Technology, networking, Electronic and Automation Control Conference

作者： Hongge Zhao Jingli Wang Tixiang Zhang Huijuan Hao 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) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Jinan Eco-environmental Bureau Pingyin Branch Office Jinan China Shandong Unite Environmental Technology Co Jinan China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

With the rapid development of urbanization and industrialization, atmospheric pollution has become a major environmental problem of global concern. The problem of atmospheric pollution not only destroys the ecological en-vironment of the earth, but also has a serious impact on people's health and development. Therefore, accurate prediction of atmospheric monitoring data is of great significance to environmental management as well as public health. To improve the accuracy of prediction, we propose a prediction method for atmospheric monitoring data based on Trans-former and Generative Adversarial network (GAN). Firstly, data pre-processing is performed on the collected atmospheric monitoring data, and then, constructing a prediction model based on Transformer and GAN for atmospheric monitoring data, aiming at extracting the data features of atmospheric monitoring data by Transformer and generating the data by using the generator of GAN. In addition, we improve the position encoding (PE) of the Transformer in order to it can efficiently extract temporal features of atmospheric monitoring data. Finally, the effectiveness of the proposed method is verified by data from three air monitoring stations in a city.

关键词： Accuracy Pollution Atmospheric modeling Urban areas Predictive models Generative adversarial networks Transformers Feature extraction Data models Monitoring

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