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作者： Li, Qiliang Cui, Jie Zhong, Hong Du, Yichao Luo, Yonglong Liu, Lu School of Computer Science and Technology Anhui University Hefei China Anhui Provincial Key Laboratory of Network and Information Security Wuhu China School of Electronics Computing and Mathematics University of Derby DerbyDE22 1GB United Kingdom

Elevator scheduling algorithms generally denote methods used to calculate how to use the elevator. These algorithms can distribute elevators to various floors of a building, thereby achieving efficient transportation. From the perspective of the elevator scheduling problem, we address the load-balancing problem for software-defined networking (SDN) architecture and propose a load-balancing method based on the elevator scheduling algorithm, LBBESA. We take advantage of the flexibility of the SDN architecture, obtain the real-time load of the server through real-time statistical analyses of the SDN switch port traffic by the controller, and combine this with the idea of regional elevator allocation to coordinate the connection of the client's requests and realize the load balancing of each server in the cluster. Simulation experiments show that, compared with the round-robin algorithm, LBBESA is more effective in the load balancing of the server pool and can improve the throughput of the server pool to a certain extent. In addition, our scheme is easy to implement and has high scalability. © 2019 John Wiley & Sons, Ltd.

关键词： Software defined networking

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BCE-PPDS: Blockchain-based cloud–edge collaborative privacy-preserving data sharing scheme for IoT

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Future Generation Computer Systems 2026年 174卷

作者： Guijuan Wang Qi Liu Zhongyuan Yu Hongliang Zhang Anming Dong 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 Shandong 250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan Shandong 250353 China China University of Petroleum Qingdao Shandong 266000 China

Internet of Things (IoT) devices generate large amounts of data every day that can be combined with intelligent platforms for predictive analytics and scientific research. However, concerns about privacy and security hinder the willingness of individuals to share data. Blockchain emerged as a promising infrastructure for facilitating secure data sharing due to its decentralized, immutability, and auditable benefits. In this paper, we propose a blockchain-based cloud–edge collaborative privacy protection data sharing scheme (BCE-PPDS), which is decentralized and enables data requesters (DRs) to search data resources using smart contracts to efficiently obtain target data. To protect the identity privacy of data owners (DOs), we propose a novel certificateless linkable ring signature algorithm with efficient performance. This algorithm is not only suitable for deployment on resource-limited IoT devices, so that DOs can realize anonymous identity authentication, but also can aggregate the generated ring signatures for batch verification, so as to improve the efficiency of signature verification. In addition, we designed a key distribution algorithm using the Asmuth–Bloom secret sharing scheme to ensure the security of the key. Under the random oracle model, BCE-PPDS is provably secure. The experimental results verify that BCE-PPDS is efficient and practical.

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A Three-party Repeated Game Model for Data Privacy in Mobile Edge Crowdsensing of IoT 13

A Three-party Repeated Game Model for Data Privacy in Mobile...

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13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020

作者： Zhao, Mingfeng Chen, Lei Xiong, Jinbo Tian, Youliang Fujian Provincial Key Laboratory of Network Security and Cryptology College of Mathematics and Informatics Fujian Normal University Fuzhou350117 China College of Engineering and Computing Georgia Southern University GA30458 United States State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9781631902710

The low request response delay of mobile edge crowdsensing (MECS) paradigm allows quick interactions among entities in practical scenarios. However, there often exist dishonest behaviors in such interactions, and the personal information leakage involved seriously threatens the privacy and security of sensing users. To tackle this problem, previously we had proposed a non-repeated three-party game model, without the consideration of multiple interactions in the actual scenario. Based on game theory, this research therefore proposes a three-party repeated game model. Specifically, we propose the corresponding social norms for different phases of sensing data. It analyzes all possible behaviors deviating from rationality, calculates the change of corresponding payoff function, and explores the influencing factors and constraints of players' honest behaviors based on the premise of maximizing interests. Finally, a significant number of simulations and numerical analyse indicate that the proposed model is feasible and effective in maximizing the benefits of game participants. Copyright © 2020 EAI.

关键词： Game theory

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SRIF: Data-Free Knowledge Distillation via Stable Regulation and Input Filtering

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Zherui Xu, Rongtao Wang, Changwei Xu, Shibiao Zhou, Jie Xu, Wenhao Gao, Longxiang Xu, Wenbo Guo, Li Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

Data-free knowledge distillation (DFKD) enables knowledge transfer from a pre-trained teacher to a student network without accessing the real dataset. However, generator-based DFKD methods struggle to ensure that the synthetic images accurately reflect the real dataset distribution. The update of the generator network relies heavily on teacher category guidance, but varying teacher prediction accuracy across categories leads to inconsistent synthetic image quality. Such variations introduce a distribution shift between synthetic and real datasets, negatively impacting student network performance during knowledge distillation. To address this challenge, we propose the SRIF, comprising two components: Student-Driven Flexible Filtering (SDFF) and Re-weighting for Independent Regularization (RIR). SDFF filters out synthetic images affected by the category distribution shift during data generation, producing a more reliable dataset. RIR, applied during distillation, encourages the student to learn stable causal relationships through sample reweighting. Both components flexibly integrate into existing DFKD frameworks, improving performance while reducing training costs. © 1999-2012 IEEE.

关键词： Photointerpretation

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WebIDE cloud server resource allocation with task pre-scheduling in IOT application development 2

WebIDE cloud server resource allocation with task pre-schedu...

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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Wang, Huaijun Li, Junhuai Tian, Jubo Wang, Kan Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xian University of Technology Xian710048 China

ISBN: (纸本)9781728129778

WebIDE is leveraged for IOT applications development, which could adapt to the rapid growth of IOT applications and meanwhile facilitate the rapid development. Resource allocation is of vital significance in the WebIDE cloud service system. Existing resource allocation approaches may encounter issues such as unbalanced resource assignments, which could lead to the reduced system resource utilization or extended system response time. The fundamental reason behind it lies in the fact that existing ones are typically on the basis of predetermined resource demands for each task, and not applicable to the case with dynamic and unknown resource demands. Thus, in this paper, we propose a novel approach based on task pre-scheduling, which schedules beforehand each task into a virtual machine local task queue by means of demand prediction. Firstly, all tasks are classified, based on the execution state, execution operations and cloud server resource requirements. Secondly, the grouped tasks are mapped to different system states, with the Markov state transition probability matrix leveraged to model the transition probability between tasks, followed by the prediction model constructed. Finally, integrating both task pre-scheduling and ant colony algorithm, cloud server resource allocation is carried out. Simulation results show that the task prediction model could significantly not only reduce the task response time, but also improve the cloud server resource utilization. © 2019 IEEE.

关键词： Resource allocation

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Segment Anything Model is a Good Teacher for Local Feature Learning

arXiv

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arXiv 2023年

作者： Wu, Jingqian Xu, Rongtao Wood-Doughty, Zach Wang, Changwei Xu, Shibiao Lam, Edmund Y. The University of Hong Kong Pokfulam Hong Kong The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China Northwestern University EvanstonIL60201 United States The 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 Jinan250013 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in any scene and any downstream task. Data-driven local feature learning methods need to rely on pixel-level correspondence for training. However, a vast number of existing approaches ignored the semantic information on which humans rely to describe image pixels. In addition, it is not feasible to enhance generic scene keypoints detection and description simply by using traditional common semantic segmentation models because they can only recognize a limited number of coarse-grained object classes. In this paper, we propose SAMFeat to introduce SAM (segment anything model), a foundation model trained on 11 million images, as a teacher to guide local feature learning. SAMFeat learns additional semantic information brought by SAM and thus is inspired by higher performance even with limited training samples. To do so, first, we construct an auxiliary task of Attention-weighted Semantic Relation Distillation (ASRD), which adaptively distillates feature relations with category-agnostic semantic information learned by the SAM encoder into a local feature learning network, to improve local feature description using semantic discrimination. Second, we develop a technique called Weakly Supervised Contrastive Learning Based on Semantic Grouping (WSC), which utilizes semantic groupings derived from SAM as weakly supervised signals, to optimize the metric space of local descriptors. Third, we design an Edge Attention Guidance (EAG) to further improve the accuracy of local feature detection and description by prompting the network to pay more attention to the edge region guided by SAM. SAMFeat’s performance on various tasks such as image matching on HPatches, and long-term visual localization on Aachen Day-Night showcases its superiority over previous local features. The release code is available at https://***/vignywang/SAMFeat

关键词： Computer vision

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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

SSRN

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SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

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Crsanet: Class Representations Self-Attention network for the Segmentation of Thyroid Nodules

SSRN

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SSRN 2023年

作者： Sun, Shiyao Fu, Chong Xu, Sen Wen, Yingyou Ma, Tao School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China General Hospital of Northern Theatre Command Shenyang110016 China Medical Imaging Research Department Neusoft Research of Intelligent Healthcare Technology Co. Ltd. Shenyang China Dopamine Group Ltd. Auckland1542 New Zealand

Segmenting accurate thyroid nodules in medical ultrasound images is always non-trivial due to the large variation in size, shape and texture of nodules, and also the presence of surrounding tissues and organs with similar intensity values. Existing algorithms solve this by designing multi-scale spatial context modeling modules and introducing the emerging self-attention based paradigm, while failing to learn the class-wise relational context around nodules. To this end, we propose a novel coarse-to-fine framework, namely, CRSANet, which leverages both class-wise relational context and spatial multi-scale context for comprehensive context perception. Specifically, we first introduce the concept of Class Representations, which describe the overall representation of thyroid nodules in ultrasound images from a categorical perspective. Secondly, we propose a backbone-free dual-branch Class Representation Self-Attention Module (CRSA) to refine the coarse segmentation results from segmentation backbones. Concretely, Fine Class Region (FCR) branch is proposed to impose consistency between pixels and their corresponding Class Representations to correct the wrongly classified pixels. Meanwhile, Class Contextual Representation (CCR) branch is introduced to enhance the ability of pixel representations with the corresponding Class Representations. We evaluate the effectiveness and generality of CRSANet using three types of backbone networks. In the experiments, our proposed U-CRSANet achieves a mIoU of 78.86% and 81.35%, an accuracy of 97.11% and 98.63%, a Dice value of 74.26% and 87.57%, respectively, which outperforms most state-of-the-art methods on the public DDTI and TN-SCUI datasets. © 2023, The Authors. All rights reserved.

关键词： Pixels

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Risk Modeling and Traceability of Boundary Attacks on Internet of Power Systems Based on Complex networks

Risk Modeling and Traceability of Boundary Attacks on Intern...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Xin Li Zhaoyang Qu Tong Yu Ming Xie Fu Yu Wei Ding School of Electrical Engineering Northeast Electric Power University Jilin China People’s Bank of China Qinghai Provincial Branch Xining China China Southern Power Grid Guangxi Power Grid Co. Ltd. Research Institute Nanning China Bijie Power Supply Bureau China Guizhou Power Grid Co. Ltd. Bijie 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

With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to keep up with the rapid development of network technologies and applications. Additionally, they fail to trace and source network attacks, leading to frequent organized and purposeful malicious attack incidents. To address these issues, this paper proposes a research method for risk modeling and traceability of boundary attacks on power IoT terminals based on complex networks. First, based on the node information of power IoT terminal devices and complex network theory, a terminal boundary attack network (TBAN) is established. Next, using indicators related to complex network theory, such as degree, in-degree, and out-degree, the terminal boundary attack network is analyzed. Furthermore, a staged TBAN is proposed to analyze the attack risks at different times. Then, combining causal theory, a method for tracing and sourcing terminal boundary attacks based on a causal Bayesian network is proposed to identify and trace malicious attacks. Finally, validation and testing analysis are conducted using the actual deployment of power IoT terminal devices. The proposed analysis method transforms the spatial characteristics of the power IoT terminal boundary into a temporally sequential TBAN, which not only visually reflects attack risks but also reveals the temporal relationships of terminal boundary attacks between lines.

关键词： Complex networks Transforms Data science Data models Bayes methods Power systems Internet security Data communication Testing

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Chinese named entity recognition method for the field of network security based on RoBERTa

Chinese named entity recognition method for the field of net...

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International Conference on networking and network Applications (NaNA)

作者： Xiaoyan Zhu Yu Zhang Lei Zhu Xinhong Hei Yichuan Wang Feixiong Hu Yanni Yao School of Computer Science and Engineering Xi’an Jiaotong University Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China School of Computer Science and Technology Xi’an University of technology Xi’an China Shenzhen Tencent Computer System Co. Ltd Shenzhen China

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively.

关键词： Text recognition Cellular phones Semantics Neural networks network security Feature extraction Internet

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