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A Solution Supporting Secure Transmission of Big Data

Tsinghua science and technology 2023年第5期28卷 838-848页

作者： Haonan Ling Yan Gao Huibin Wang Ming Chen Institute of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China China Electronic Systems Engineering Corporation Beijing 100840China College of Computer and Information Engineering Chuzhou UniversityChuzhou 239000

Traditional Internet protocol networks cannot provide the service of selecting a secure path to transmit various types of data with specific security ***,to solve the“secure path transmission”problem,this paper first proposes a search-transmit model for secure network path transmission,i.e.,to find a multi-attribute optimized path that meets the specific security requirements in the search phase,and then transmit the packets along the optimized path in the transmission ***,we propose a solution to the search-transmit *** idea of the solution is to use the particle swarm optimization algorithm to search for a secure path that meets the multi-attribute requirements and then set the source route on the router to control the packet ***,a prototype system based on network function virtualization is developed to evaluate the feasibility and performance of the proposed *** results show that the proposed solution outperforms existing algorithms in terms of performance.

关键词： network secure path search-transmit model multi-attribute path optimization segment routing network function virtualization

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Residual deep fuzzy system with randomized fuzzy modules for accurate time series forecasting

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Neural Computing and Applications 2024年第17期36卷 9989-10014页

作者： Liu, Yunxia Peng, Wei Wang, Haixia Li, Chengdong Lu, Xiao College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China Shandong Key Laboratory of Intelligent Buildings Technology School of Information and Electrical Engineering Shandong Jianzhu University Jinan250101 China College of Energy Storage Technology Shandong University of Science and Technology Qingdao266590 China Engineering Training Center Shandong University of Science and Technology Qingdao266590 China

The data-driven modular deep fuzzy model has demonstrated excellent forecasting performance due to its clear architecture and powerful fuzzy inference ability. However, the fixed structure predesigned for specific types of datasets limits the further improvement of generalization ability, especially when facing high-dimensional datasets with the challenging problem of rule explosion, thereby suffering from the time-consuming training process. To overcome these limitations, a novel randomized fuzzy modules (RFMs) stacked residual deep fuzzy system (RFM-RDFS) is proposed in this study. Firstly, to obtain high-precision outputs, this paper presents a residual deep fuzzy framework by stacking RFMs layer by layer, where the modules in the current layer only learn the residual of the previous layer. Secondly, to improve the design freedom for different applications, the RFM with multiple inputs is raised, where the input variables are allocated by a sliding window along the input spaces of the layers, so the number of the parallel modules in the same layer can be flexibly adjusted. Meanwhile, in the RFM, inspired by the extreme learning machine, the centers and widths of Gaussian membership functions are randomly generated, which can greatly accelerate the training speed. Thirdly, to strengthen the approximation performance, the consequent parameters of the fuzzy rules in each RFM are learned by minimizing the loss function with the regularization terms through the ridge regression algorithm. Finally, extensive experiments on various datasets are conducted to compare the proposed RFM-RDFS with several state-of-the-art shallow and deep models. The detailed comparison results clearly show that the proposed RFM-RDFS is more flexible and compact, and has greater advantages in terms of the interpretability, generalization ability and robustness. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Improvement of Seafloor Positioning Through Correction of Sound Speed Profile Temporal Variation

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1099-1101页

作者： Miao Yu Kuijie Cai Cuie Zheng Dajun Sun Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001 Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001 College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China College of Mathematical Sciences Harbin Engineering UniversityHarbin 150001China

Dear Editor,This letter proposes a high-precision seafloor transponder positioning method based on the correction of sound speed profile(SSP)temporal *** the proposed method,the ocean sound speed error is modeled as the temporal variation of a background SSP,and the linearized expression of the acoustic travel time with respect to the sound speed coefficient is derived based on the ray acoustic ***,the proposed method introduces the constraint of acoustic ranging observations between seafloor transponders and determines the weights of travel time and ranging observations using Akaike’s Bayesian information criterion(ABIC)to reduce the positioning error caused by the correlation between sound speed and position *** experimental results in the South China Sea show that the proposed method performs better than the global navigation satellite system-acoustic ranging combined positioning solver(GARPOS)[1],in terms of rigid distance errors and long baseline positioning accuracy.

关键词： ocean travel sound

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H_(2)O_(2) treatment boosts activity of NiFe layered double hydroxide for electro-catalytic oxidation of urea

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Journal of Environmental sciences 2023年第7期129卷 152-160页

作者： Jinshan Wei Jin Wang Xiaoming Sun College of Materials Science and Engineering Key Laboratory of Optoelectronic Devices and SystemsCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China State Key Laboratory of Chemical Resource Engineering College of ChemistryBeijing University of Chemical TechnologyBeijing 100029China

Urea oxidation reaction(UOR)provides a method for hydrogen production besides wastewater treatment,but the current limited catalytic activity has prevented the ***,we develop a novel H_(2)O_(2) treatment strategy for tailoring the surface oxygen ligand of NiFe-layered double hydroxides(NiFe-LDH).The sample after H_(2)O_(2) treatment(NiFeOLDH)shows significant enhancement on UOR efficiency,with the potential of 1.37 V(RHE)to reach a current density of 10 mA/cm^(2).The boost is attributed to the richness adsorption O ligand on NiFeO-LDH as revealed by XPS and Raman *** calculation indicates formation of two possible types of oxygen ligands:adsorbed oxygen on the surface and exposed from hydroxyl group,lowered the desorption energy of CO_(2) product,which lead to the lowered onset *** strategy is further extended to NiFe-LDH nano sheet on Ni foam to reach a higher current density of 440 mA/cm^(2) of UOR at 1.8 V(RHE).The facile surface O ligand manipulation is also expected to give chance to many other electro-catalytic oxidations.

关键词： NiFe-LDH Urea oxidation reaction H_(2)O_(2)treatment Electro-catalysis Hydrogen production

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Fine-Grained Anomaly Detection on Dynamic Graphs via Attention Alignment 40

Fine-Grained Anomaly Detection on Dynamic Graphs via Attenti...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chen, Dong Zhao, Xiang Xiao, Weidong National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology Laboratory for Big Data and Decision China

ISBN: (纸本)9798350317152

Dynamic graphs are ubiquitous in our lives, yet they are also susceptible to the risks imposed by malicious activities. However, identifying anomalies in these dynamic graphs presents a challenging task due to the complex graph structures. Existing methods for dynamic anomaly detection primarily focus on learning representations for each timestamp and using sequence modeling techniques to capture and model the temporal information. Despite extensive research, dynamic anomaly detection still faces two key challenges. First, existing methods are limited in effectively using fine-grained temporal information. Second, they have limited generalization capabilities under unsupervised settings. Overcoming these challenges is crucial for advances in dynamic anomaly detection. In this paper, we propose a novel unsupervised anomaly detection method for dynamic graphs. Our approach leverages complex temporal information through fine-grained sampling and embedding modules. Additionally, we introduce an attention alignment strategy to minimize discrepancies in contextual attention between source and target nodes. Through a comprehensive evaluation, we demonstrate that our strategy effectively mitigates overfitting and improves generalization. Experiments on ten dynamic graph datasets validate the effectiveness of our proposed method in detecting anomalies. © 2024 IEEE.

关键词： Anomaly detection

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, J.E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications 15772 Greece Keio University Department of Information and Computer Science Kanagawa 2238522 Japan Tohoku University International Research Institute of Disaster Science Sendai9808576 Japan

As synthetic aperture radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this article proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network. First, employing Multiscale Superpixel Reconstructed Difference Image effectively enhances the edge information of the images. Second, a two-stage Weighted Center Constrained Fuzzy C-Means clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Third, a Convolutional Squeeze-and-Excitation Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model is introduced into the CSWNN to expand pseudolabeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2% –9% better than the classical algorithm. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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