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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Kuan Liu Yumei Li Yuanhua Qi Ning Qi Mengran Zhai Information Research Institute of Shandong Academy of Sciences Qilu University of Technology (Shandong Academy of Sciences) 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) 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Shandong Institute of Economy and Informatization Development Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the development of the digital economy and the advent of the big data era, the rapid growth of textual information in cyberspace has placed higher demands on information processing technology. This paper proposes an information extraction technique based on the Text-to-Text Transfer Transformer, known as T5, and keyBERT models, aiming to efficiently distill the key content from textual information in cyberspace. This method combines automatic summarization and keyword extraction to form information briefs. The experiments selected representative policy documents related to data elements as input data and employed information entropy and ROUGE scores as metrics to evaluate the automatic summarization model. The results were compared with the outputs of similar models. Experimental results indicate that the T5 model outperforms other models in terms of summarization effectiveness, and the proposed information extraction method shows significant advantages in readability and processing efficiency, demonstrating practical application value.

关键词： Measurement Biological system modeling Cyberspace Text summarization Information processing Information retrieval Transformers Data models Data mining Information entropy

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Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model

Intelligent recommendation of related items based on naive b...

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2020 International Conference on Machine Learning and Computer Application, ICMLCA 2020

作者： Wei, Wei Wang, Zhuo Fu, Changlong Damaševičius, Robertas Scherer, Rafal Wožniak, Marcin School of Computer Science and Engineering Xi'An University of Technology Xi'an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology China

Nowadays, data plays a unique role in various fields. Based on the background of the era of big data, this paper collects user evaluations of certain commodities and labels the evaluations into positive emotions and negative emotions. We propose an intelligent recommendation algorithm based on Naive Bayes algorithm, which determines the user's preference for a product according to the user's opinion on a product, then uses the collaborative filtering algorithm to recommend other similar products according to the similarity between the product and other products, so as to realize the recommendation of other similar products. This can not only help users choose more goods in the environment, but also maximize the value of goods. In this paper, bayesian classifier was used for prediction, and the accuracy reached about 97%. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative filtering

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A Merge Collision Prediction TDMA-MAC Protocol in Distributed VANET

A Merge Collision Prediction TDMA-MAC Protocol in Distribute...

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Zhang, Chang-Yue Han, Shi-Yuan Zhou, Jin Li, Bao-Zhu Yu, Xiao-Jie Yao, Kang University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Zhuhai Fudan Innovation Institute Internet of Things and Smart City Innovation Platform Zhuhai518057 China Shandong Provincial Network Security and Infomationization Technology Center Jinan250002 China

ISBN: (纸本)9781665402453

The communication between vehicles has attracted more and more attention in intelligent transportation system(ITS). Due to the rapid movement of vehicles, the topology of network nodes changes rapidly, resulting in collisions between vehicles. Therefore, the design of efficient and reliable media access control protocol is very necessary to solve the communication problem between vehicles. In this paper, a merge collision prediction TDMA-MAC in distributed VANET(MCPMAC) is proposed to solve the merging collision problem. In MCPMAC, when multiple vehicles occupying the same time slot meet within the two hop communication range, one vehicle continues to use the time slot by comparing the time stamp of the vehicle access time slot, and the other vehicles sequentially select the idle time slot to reduce the probability of merging collision between vehicles. Simulation results show MCPMAC protocol has higher reliability and lower number of collisions than the existing MAC protocols. © 2021 IEEE.

关键词： Vehicles

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A Multi-Path Orchestration Method for Cloud-Edge Data Transmission Based on DRL

A Multi-Path Orchestration Method for Cloud-Edge Data Transm...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhihui Wang Xubing Dou Shuyun Li Huan Ma Xinchang Zhang Maoli Wang 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 Shandong Normal University Jinan China QuFu normal university Jining China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In addressing the multi-path problem of cloud-edge data transmission, this paper introduces a multi-path orchestration method based on deep reinforcement learning (DRL) within the context of software-defined networks (SDN), termed Deep Q-Routing (DQR). This method establishes optimal data transmission paths between the cloud and multiple edges, based on available bandwidth and transmission delay, while avoiding path congestion to improve bandwidth utilization and reduce transmission delay. The optimal routing in SDN is defined through the design of state space sets, reward functions, and neural networks. Simulation experiments were conducted to evaluate the DQR method. Compared to existing routing methods, the proposed DQR method, which includes a reward function with weighted parameters, demonstrates superior overall network performance.

关键词： Cloud computing Scalability Neural networks Distributed databases Bandwidth Routing Deep reinforcement learning Delays Data communication Software defined networking

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

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

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Zhitao Yu Peng Sun Mingle Zhou Qianlong Liu Shilong Zhao Hisense Video Technology Co. Ltd Qing Dao China Hisense Visual Technology Co. Ltd Qing Dao 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

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.

关键词： Energy consumption Heuristic algorithms Brightness Light emitting diodes Liquid crystal displays Robustness Entropy Energy efficiency Vehicle dynamics Optimization

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Demand Response Scheme: Achieving Sustainability in Distributed Data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou 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

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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FusionNet: An Effective network Phishing Website Detection Framework Based on Multi-Modal Fusion

FusionNet: An Effective Network Phishing Website Detection F...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Yu Sun Guangqi Liu Xiaohui Han Wenbo Zuo Weihua Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) (Shandong Academy of Sciences) Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quancheng Provincial Laboratory Jinan China

Recently, the rapid advancement of communication technology has undoubtedly improved people's lives. Unfortunately, this progress has also given rise to a significant issue: the proliferation of phishing websites. Regrettably, phishing web sites have now emerged as one of the primary threats to cybersecurity. While there have been extensive efforts to develop anti-phishing techniques, many of these approaches focus solely on a single modality of information, such as the website's URL, HTML source code, or visual features. Given the growing sophistication of phishing attacks, relying on just one modality of information is no longer sufficient to accurately identify phishing websites. To tackle this challenge, our research aims to leverage the complementarity of various modalities to enhance phishing website detection. In this paper, we introduce a multi-modal framework called FusionNet to identify phishing web pages. This framework utilizes three distinct information modalities: URLs, HTML source codes, and visual features. Specifically, our approach first designs specific representation learning architectures tailored to each modality's unique properties. Subsequently, we incorpo-rate an attention mechanism to merge these representations, thereby exploiting the synergies between the multi-modal data. The learned feature representations are more discriminative, contributing to significantly improved phishing website detection accuracy. To evaluate the performance of FusionNet, we collect a dataset comprising phishing webpages, each sample contains all three modalities-URLs, HTML source codes, and visual features. Our extensive experiments conducted on this dataset demonstrate that FusionNet outperforms state-of-the-art methods in terms of both robustness and accuracy.

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FedLRDP: Federated Learning Framework with Local Random Differential Privacy

FedLRDP: Federated Learning Framework with Local Random Diff...

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International Joint Conference on Neural networks (IJCNN)

作者： Runtian Zhou Anming Dong Jiguo Yu Qingyan Ding 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 School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Federated learning (FL) is a distributed machine learning framework enabling multiple clients to collaboratively train a shared ML model without sharing raw data. Despite its aim to safeguard data security and privacy, FL faces risks from advanced adversarial attacks like membership inference attack (MIA), potentially leaking sensitive information. To counter these threats, differential privacy (DP) methods add noise to shared model parameters. However, traditional DP methods struggle with balancing model accuracy, training efficiency, and privacy protection, often compromising one for the other. Additionally, uniform DP mechanisms may not cater to varying client privacy and performance needs. To tackle these challenges, we propose a federated learning framework based on localized random differential privacy (FedLRDP). This approach empowers each client to control noise levels based on their privacy requirements, enhancing model performance while preserving data privacy. We further optimize client-side loss functions to enhance model performance. Theoretical analysis establishes the convergence bounds for FedLRDP, demonstrating its superior convergence performance. Experimental results on MNIST reveal that FedLRDP improves model accuracy by 3.28% compared to traditional DP methods while mitigating MIA with a 72.48% lower attack success rate than FedAvg. Moreover, on EMNIST, FedLRDP boosts model accuracy by 7.31% compared to traditional DP methods, maintaining a 59.4% lower attack success rate than FedAvg. These findings underscore FedLRDP’s efficacy in meeting client privacy needs while enhancing model performance.

关键词： Training Differential privacy Privacy Accuracy Federated learning Noise Data models

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Communication-Efficient Federated Learning for network Traffic Anomaly Detection

Communication-Efficient Federated Learning for Network Traff...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Xiao Cui Xiaohui Han Guangqi Liu Wenbo Zuo Zhiwen Wang 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 Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Quancheng Provincial Laboratory Jinan China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

As an emerging machine learning method, Federated Learning is widely used in network anomaly detection scenarios. However, many current federated learning-based network anomaly detection works ignore the communication overhead problem during model training. The transmission of model parameters occupies a large amount of bandwidth, affecting the efficiency of network anomaly detection as well as the communication tasks of other programs. In this study, we propose eFedAD, an efficient federated learning framework for network traffic anomaly detection. Specifically, eFedAD uses singular value decomposition to compress the transmitted parameters and introduces a weighting mechanism to control the compression rate and aggregation proportion. In this way, eFedAD can significantly reduce the number of transmitted parameters and assign higher weights to more valuable clients, enhancing the generalization ability of the global model. Additionally, to address the slow convergence issue due to compressed parameters and minimize the total number of transmitted parameters, eFedAD uses a client selection method that is based on clustering clients’ data feature distributions. The experimental results demonstrate that eFedAD outperforms other compression methods and network anomaly detection approaches, achieving excellent performance.

关键词： Training Data privacy Federated learning Telecommunication traffic Data models Sensors Task analysis

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TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Federated learning

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