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检索条件"机构=Laboratory for Network Computing and Security Technology"

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Petri net modeling and vulnerability analysis of the Heartbleed

Petri net modeling and vulnerability analysis of the Heartbl...

引用

2021 International Conference on networking and network Applications, NaNA 2021

作者： Wang, Yichuan Wang, He Hei, Xinhong Ji, Wenjiang Zhu, Lei Xi'an University of technology School of computer science and engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology China

ISBN: (纸本)9781665441582

In recent years, a variety of network attacks emerge in an endless stream, and network attacks gradually show the characteristics of higher secrecy and greater harm. At present, the analysis of system vulnerabilities is generally focused on the characteristics analysis and impact hazard level, and lack of formal modeling and vulnerability analysis methods. In this paper, we model the OpenSSL service's Heartbleed vulnerability based on Petri net. We build a formal model combined with the source code and system state, analyze the vulnerability of the system running state, and propose an automatic vulnerability repair scheme. Experiments show that this model can carry out fine-grained formal analysis and simulation of the Heartbleed, which is of great significance to explore the system vulnerability modeling method. © 2021 IEEE.

关键词： Petri nets

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A Short-Term Electric Load Forecasting Method Based on the Fusion of Global and Local Features 9

A Short-Term Electric Load Forecasting Method Based on the F...

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Gu, Haozhan Tian, Hangwei Wang, Jingchun Li, Yumei Shandong University Of Arts Academy of Fine Arts China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

Accurate short-term electric load forecasting is crucial for ensuring the stability and efficiency of the power grid. This study proposes an innovative dual-branch electric load forecasting model that enhances the accuracy of predictions by processing global and local features in parallel. Global features are extracted through the Savitzky-Golay filter and a linear model to capture the overall trends of the load data. Local features are identified using the Convolutional Self-Attention Module, which integrates the local perception capabilities of Convolutional neural network with the self-attention mechanism of Transformers. By employing a Weighted Feature Fusion network, we effectively integrate global and local information to enhance the performance of load forecasting. Experimental results demonstrate that the proposed model significantly outperforms existing models in predictive performance. The model presented in this study offers a new perspective on electric load forecasting and has the potential to become an effective tool for power system planning and scheduling. © 2024 IEEE.

关键词： Deep learning Feature fusion Load prediction Time series data analysis

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Self-Paced Self-Training Based on K-Nearest Neighbors 4

Self-Paced Self-Training Based on K-Nearest Neighbors

引用

4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Ma, Yongzheng Sun, Zhengkai Zhang, Yintong Guan, Hongjiao Qiao, Xinxiao 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

ISBN: (纸本)9798400709449

Self-training is a classical semi-supervised classification method, which iteratively trains labeled samples that are enhanced using unlabeled samples. However, traditional self-training selects pseudo-labeled examples without distinguishing between different classes. Furthermore, wrong pseudo-labeled samples accumulate, leading to an increase in learning bias. In this paper, we propose a self-paced self-training based on K-nearest neighbors (SPST-KNN). SPST-KNN is composed of three main components: sample difficulty calculation, sample type determination, and self-paced selection based on KNN. The types of examples, including safe, boundary, and outlying examples, are determined by KNN. According to the principle of self-paced learning, SPST-KNN preferentially selects easy pseudo-labeled examples in each sample type to be added into the labeled sample set. Experiments and ablation study on 20 real-world datasets have demonstrated the superiority and effectiveness of the proposed SPST-KNN. © 2023 ACM.

关键词： Iterative methods

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A Global Task Scheduling Method Based on network Measurement and Prediction in computing Power networks 29

A Global Task Scheduling Method Based on Network Measurement...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Chuanfu Li, Wen Chen, Jing Ge, Jing Wang, Di Li, Na Sun, Hao Geng, Yudong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

To improve the task scheduling efficiency in computing power networks, this paper proposes a global task scheduling method based on network measurement and prediction in computing power networks (GTS-MP), which selects a more suitable computing power cluster and storage platform with a higher network availability score between them to implement tasks. The experiments are conducted on a test bench of urban area networks in 12 cities. The results show that this method greatly reduces the execution time of artificial intelligence model training tasks, with a maximum reduction of 40% compared to other plans. © 2023 IEEE.

关键词： computing power network global scheduling network availability score task offloading

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Method and system of power load classification and decomposition based on deep learning 4

Method and system of power load classification and decomposi...

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4th International Conference on Smart Grid and Energy Engineering, SGEE 2023

作者： Sun, Hao Yuan, Hongtai Geng, Yudong Chen, Jing Zhang, Chuanfu Li, Wen Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350359619

This paper introduces a system for the classification and decomposition of electrical loads, addressing the shortcomings of current non-intrusive load decomposition methods that are prone to errors. We propose a method that first classifies electrical loads and then performs decomposition, along with the corresponding system workflow. Initially, we collect household electricity consumption data using a proprietary load collection device developed by the company, constructing an electrical load dataset. Subsequently, we train classification and decomposition methods using this dataset, conducting comparative experiments with various methods to select the most effective one. Based on the trained electrical load classification and decomposition models, we develop an electrical load inference program. The key implementation lies in applying the electrical load classification and decomposition inference program to edge devices. Leveraging the cloud server hosting a backend web service for the mobile app, we interact with users through the app, achieving precise decomposition and monitoring of electrical loads. Integrating edge computing and cloud computing, coupled with a mobile app serving as the interaction platform, this paper presents a high-precision, simplified, and highly scalable solution for electrical load analysis and management. In experimental comparisons, we find that using the Gated Recurrent Unit (GRU) method from deep learning and the Sequence-to-Point Neural network (Seq2point) method outperform other methods in classification and decomposition, significantly improving the accuracy of electrical load classification. Therefore, we select the GRU method and Seq2Point method as the core techniques for the electrical load classification and decomposition system. This system not only demonstrates significant advantages in performance but also possesses high accuracy, model simplification, and scalability, providing an efficient and reliable solution for elec

关键词： Edge computing

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GMD-YOLOv5s: An Improved Steel Surface Defect Detection Algorithm Based on YOLOv5s

GMD-YOLOv5s: An Improved Steel Surface Defect Detection Algo...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Zhike Su Aimin Li Zheqi Yang Mian 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 Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Current steel surface defect detection methods have the shortcomings of low accuracy, poor real-time performance and limitation to detect small targets. To solve those problems, we proposed a lightweight detection model named GMD-YOLOv5s. Firstly, we introduce the C3 Global Ghost (C3-GGhost) module, an enhancement of the original C3 module, which improves both detection speed and accuracy for industrial applications. Secondly, Multi-Scale Dilated Attention (MSDA) is integrated into the neck network to capture target at different scales. Because MSDA can expand the model's receptive field and feature representation capabilities. Finally, in order to enhance the model's ability to detect small targets, we replace the conventional detection head with a Distributed Pixel-level Feature Refinement Head (DPFRHead). Experimental results show that GMD-YOLOv5s achieves a mean Average Precision (mAP) of 82.9% on the NEU-DET dataset. Our method improves by 30% compared to the baseline YOLOv5, while reducing the number of parameters by 60%. Ablation and comparative experiments validate the model's effectiveness and accuracy in detecting defects on steel surfaces.

关键词： YOLO Accuracy Federated learning Computational modeling Heuristic algorithms Feature extraction Real-time systems Neck Steel Defect detection

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Key Management and Authentication Scheme Forward Trusted Pervasive Container computing

Key Management and Authentication Scheme Forward Trusted Per...

引用

Big Data and Privacy computing (BDPC), International Conference on

作者： XinHong Hei YuZhan Xue YiChuan Wang YiFan Liu Chen Peng Xiang Sun School of computer science and engineering Xi'an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology School of computer science and engineering Xi'an University of Technology Xi’an China

ISBN: (数字)9798350307597

ISBN: (纸本)9798350307603

Cloud computing technology is an internet-based computing model that provides high reliability and security measures, including data backup, secure access control, etc. As Docker containers play a increasingly important role in traditional cloud computing architectures, the communication needs between containers become prominent. However, existing container to container communication mainly relies on network protocols for file transfer, making it difficult to achieve the expected results in cloud cluster security. Therefore, this paper primarily focuses on container based keys and proposes a container identity trusted key management method based on Pervasive container computing architecture. The experimental results demonstrate that the container identity trusted key management method not only meets the security transmission characteristics required in the container cloud architecture, but also adopts a strategy combined with trusted hardware, providing security guarantees. In addition, the method proposed in this article that combines trusted hardware for signature verification can defend against multiple network attacks compared to traditional container architectures without using security policies.

关键词： Computers Cloud computing Handwriting recognition Data privacy Protocols Computer architecture Containers

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Research on liquid crystal display technology based on regional dynamic dimming algorithm 9

Research on liquid crystal display technology based on regio...

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yu, Zhitao Sun, Peng Zhou, Mingle Liu, Qianlong Zhao, Shilong Hisense Video Technology Co. Ltd. Qing Dao China Hisense Visual Technology Co. Ltd. Qing Dao China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In vehicle liquid crystal display (LCD) technology has attracted much attention for its wide range of applications in automotive infotainment systems. However, conventional LCD technologies have limitations in terms of energy consumption and display quality, especially in the context of the increasing demand for high contrast and low energy consumption. To address these challenges, this paper carries out a study of LCD technology based on an area dynamic dimming algorithm. The technology significantly improves the contrast ratio of LCDs and effectively reduces energy consumption by dividing the backlight module into multiple independently adjustable regions and dynamically adjusting the backlight brightness of each region according to the image content. The results show that the region dynamic dimming algorithm proposed in this paper can effectively adjust the brightness of the backlight according to the image content, achieving higher display contrast and lower energy consumption. © 2024 IEEE.

关键词： Liquid crystal displays

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DNPS: A Robust Aggregation Method for Heterogeneous Distributed Learning Based on Gradient Direction and Norm Probability Screening

DNPS: A Robust Aggregation Method for Heterogeneous Distribu...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Ming Yang Caiyun Li Yunpeng He Xin Wang Xiaoming Wu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Heterogeneous distributed machine learning systems are vulnerable to Byzantine attacks, where malicious worker nodes disrupt global model convergence by submitting incorrect model updates, leading to degraded system performance. Current aggregation methods that filter based on gradient norm or direction often underperform in heterogeneous environments and struggle to defend effectively against Byzantine attacks. To address this issue, we propose the Direction-Norm Probability Screening (DNPS) algorithm-a novel approach that harmonizes gradient norm and direction to blunt these attacks. DNPS integrates gradient norm and directional information to establish a probability screening mechanism for identifying and filtering Byzantine worker nodes, thereby fortifying the system's defen-sive capabilities and enhancing its robustness against Byzantine attacks. Experimental results show the DNPS algorithm provides more effective protection under both non-attack and Byzantine attack scenarios than existing aggregation methods.

关键词： Computer aided instruction Machine learning algorithms Distance learning Federated learning System performance Filtering algorithms Information filters Robustness Protection Optimization

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Medical Entity Disambiguation with Medical Mention Relation and Fine-grained Entity Knowledge 30

Medical Entity Disambiguation with Medical Mention Relation ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lu, Wenpeng Zhang, Guobiao Peng, Xueping Guan, Hongjiao Wang, Shoujin 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 Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Data Science Institute University of Technology Sydney Sydney Australia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9782493814104

Medical entity disambiguation (MED) plays a crucial role in natural language processing and biomedical domains, which is the task of mapping ambiguous medical mentions to structured candidate medical entities from knowledge bases (KBs). However, existing methods for MED often fail to fully utilize the knowledge within medical KBs and overlook essential interactions between medical mentions and candidate entities, resulting in knowledge- and interaction-inefficient modeling and suboptimal disambiguation performance. To address these limitations, this paper proposes a novel approach, MED with Medical Mention Relation and Fine-grained Entity Knowledge (MMR-FEK). Specifically, MMR-FEK incorporates a mention relation fusion module and an entity knowledge fusion module, followed by an interaction module. The former employs a relation graph convolutional network to fuse mention relation information between medical mentions to enhance mention representations, while the latter leverages an attention mechanism to fuse synonym and type information of candidate entities to enhance entity representations. Afterwards, an interaction module is designed to employ a bidirectional attention mechanism to capture interactions between medical mentions and entities to generate the matching representation. Extensive experiments on two publicly available real-world datasets demonstrate MMR-FEK's superiority over state-of-the-art(SOTA) MED baselines across all metrics. Our source code is publicly available. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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