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15th International Conference on Communication Software and networks, ICCSN 2023

作者： Cong, Ligang Shi, Baoyu Di, Xiaoqiang Ding, Huiying Chen, Chuanhui School of Computer Science and Technology Changchun University of Science and Technology Changchun China Changchun University of Science and Technology Jilin Key Laboratory of Network and Information Security Changchun China

ISBN: (纸本)9798350329360

Accurate traffic prediction is crucial for the performance improvement of satellite networks, which can help balance network load, optimize routing, and effectively improve network performance. To better plan traffic while capturing the spatial and temporal dependencies of traffic, we propose a new neural network-based traffic prediction method for satellite networks, namely the temporal graph convolutional attention (ACW-GRU) model, which is combined with the Graph Convolutional Neural network (GCN), the Gated Recurrent Unit (GRU), and the Soft Attention mechanism. The GCN is used to analyze the complex topology to capture spatial correlation, and the GRU is used to analyze the dynamic changes of traffic data to capture temporal correlation, while the Grey Wolf Optimization (GWO) is used to optimize the hyperparameters of the GRU model, and the soft attention mechanism is used to integrate the global temporal information and adjust the importance at different time points. The importance of the traffic data at distant time points is also not ignored, thus improving the accuracy of the prediction. The simulation results show that the ACW-GRU model is superior in fitting the flow data compared with the traditional GRU, LSTM, and FARIMA models, and its root mean square error (RMSE) is reduced by 24.7%, 40.4%, and 51.2%, respectively. Therefore, the ACW-GRU model can predict the traffic data of the satellite network more accurately, thus providing clearer guidance for defining the demand of applications and the allocation of network resources. © 2023 IEEE.

关键词： Topology

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Feature Aggregation network for Memory-Based Industrial Anomaly Detection 9

Feature Aggregation Network for Memory-Based Industrial Anom...

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

作者： Li, Min Han, Delong Shao, Rui He, Jinghui City University of Macau Faculty of Data Science 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 Shandong Computer Science Center National Supercomputer Center in Jinan Jinan 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 the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet. © 2024 IEEE.

关键词： Anomaly detection

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Characterization and Properties of Bent-Negabent Functions

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Chinese Journal of Electronics 2022年第4期31卷 786-792页

作者： JIANG Niu ZHAO Min YANG Zhiyao ZHUO Zepeng CHEN Guolong School of Mathematical Sciences Huaibei Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University School of Cyber Science University of Science and Technology of China School of Computer Engineering Bengbu University

A further characterization of the bentnegabent functions is presented. Based on the concept of complete mapping polynomial, we provide a necessary and sufficient condition for a class of quadratic Boolean functions to be bent-negabent. A new characterization of negabent functions can be described by using the parity of Hamming weight. We further generalize the classical convolution theorem and give the nega-Hadamard transform of the composition of a Boolean function and a vectorial Boolean function. The nega-Hadamard transform of a generalized indirect sum is calculated by this composition method.

关键词： Boolean function Bent-negabent function Walsh-Hadamard transform Nega-Hadamard transform

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TFSG: A Self-Supervised Guided Graph Learning Method for Traffic Flow Prediction

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IEEE Internet of Things Journal 2025年第13期12卷 24345-24357页

作者： Zheng, Yingran Luo, Chao Shao, Rui Shandong Normal University School of Information Science and Engineering Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China China Mobile Shandong Co. Ltd center of network optimization Jinan250014 China

With the rapid development of the Internet of Things, a large amount of real-time traffic data has been collected. How to fully utilize these traffic data for rational planning of intelligent transportation systems is an important issue. Graph learning has proved to be potential tools for modeling complex spatiotemporal relationships in addressing traffic flow prediction problems. Existing methods usually relied on the graph structure constructed from predefined distance information between different regions. However, affected by factors such as road capacity, traffic rules etc., distance bias is non-trivial to affect traffic flow in reality. Besides, the distance based static transportation network structure is also difficult to describe the dynamic and uncertainty of road traffic in complex situations. To address the aforementioned issues, this article proposes an enhanced graph generation approach for traffic flow prediction, which is based on Temporal-Frequency Self-Supervised Guided Graph Learning (TFSG). The method progressively guides the feature latent representation to approach the target latent representation in both the time domain and frequency domain. Meanwhile, a multi-head self-attention mechanism is introduced to capture dependencies among different time series patches, constructing an enhanced graph structure for downstream prediction tasks. Experimental results first independently evaluated the qualities of the generated graphs. Subsequently, by using five public traffic datasets, the feasibility and effectiveness of the proposed method are further verified. © 2014 IEEE.

关键词： Self-supervised learning

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Resource Cooperative Scheduling Optimization Considering security in Edge Mobile networks 19th

Resource Cooperative Scheduling Optimization Considering Sec...

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19th EAI International Conference on Collaborative Computing: networking, Applications and Worksharing, CollaborateCom 2023

作者： Fang, Cheng Yang, Peng Yi, Meng Du, Miao Li, Bing Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9783031545207

With the rapid development of technologies such as the Internet of Things and artificial intelligence, the contradiction between limited user computing resources and real-time, fast, and safe processing of large amounts of data has become an urgent issue. The emergence of edge computing provides IoT applications with a low-latency, high-bandwidth, and high-performance computing service. Due to the complexity and dynamics of the edge computing environment itself, and the limited resources of the terminal, the security issue of resource collaborative scheduling in the edge mobile network has become an important research topic. Different from existing work, this paper proposes an efficient and secure multi-user resource cooperative scheduling model, which comprehensively considers resource allocation, task offloading, QoE requirements, and data security. In the model, ChaCha20 encryption technology is introduced as a security mechanism to prevent data from being maliciously stolen by attackers during the offloading process, and computing speed is used as an indicator to quantify QoE requirements. A resource collaborative scheduling algorithm that integrates security mechanisms and computing acceleration is also proposed to minimize the total cost of optimizing the edge computing system. Finally, the effectiveness and superiority of the model and algorithm are verified by simulation experiments. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Edge computing

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Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission

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Frontiers of information Technology & Electronic engineering 2024年第6期25卷 791-808页

作者： Ziyang XING Xiaoqiang DI Hui QI Jing CHEN Jinhui CAO Jinyao LIU Xusheng LI Zichu ZHANG Yuchen ZHU Lei CHEN Kai HUANG Xinghan HUO Jilin Key Laboratory of Network and Information Security Changchun 130022China School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China Information Center Changchun University of Science and TechnologyChangchun 130022China

information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing ***,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth *** content delivery networks are ill-equipped to handle such bursts due to their pre-deployed *** this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing *** strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic *** formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic *** address these challenges,we leverage federated reinforcement learning ***,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing *** software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery *** paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.

关键词： information-centric satellite network Burst traffic Content delivery Federated reinforcement learning Mixed-integer linear programming model Bloom filter Dynamic network

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Voiceprint recognition model based on Ecapa-Tdnn and ResNet 5

Voiceprint recognition model based on Ecapa-Tdnn and ResNet

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5th International Conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Jia, Chengjie Fei, Rong Li, Xiaomeng School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710004 China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510691667

In recent years, with the continuous progress of deep learning neural networks and the construction of large data sets, new breakthroughs in voiceprint verification technology for voice calls have been rapidly achieved. Deep learning-based neural networks, such as X-Vectors, ResNets, Ecapa-Tdnn, etc., are able to utilize their own network characteristics to extract rich voiceprint features, thus further improving the accuracy of voiceprint recognition, but there is still room for improvement in the performance and parameter count of the traditional models. In this paper, we propose the Ecapa2Tdnn model based on EcapaTdnn and ResNet, which has the advantage of possessing a deeper network architecture while performing better in terms of temporal modeling capability. This indicates that the model can effectively handle long time series of speech data, thus improving the performance of the voiceprint recognition system. The experimental results show that under the same experimental environment, compared with the equal error rate (EER) of 11.31% of the EcapaTdnn model, the Ecapa2Tdnn model proposed in this paper has only an EER of 10.27%, which is 1.04% lower than the former EER, and the number of parameters is still one-fourth of the original model. The model improves accuracy while reducing the total number of parameters, which is expected to be widely used in the field of voiceprint recognition. © 2025 SPIE.

关键词： Deep neural networks

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IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Efficient Wildcard Searchable Symmetric Encryption with Forward and Backward security 1st

Efficient Wildcard Searchable Symmetric Encryption with For...

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1st International Conference on Artificial Intelligence security and Privacy, AIS and P 2023

作者： Zhang, Xi Su, Ye Wei, Zhongkai Shen, Wenting Qin, Jing College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Mathematics Shandong University Jinan250100 China College of Computer Science and Technology Qingdao University Qingdao266071 China

ISBN: (纸本)9789819997848

Wildcard Searchable Symmetric Encryption can achieve flexibility and pattern matching while protecting data privacy. There is a promising future for combining secure wildcard search with emerging technologies such as AI. However, there are challenges in reducing the communication cost and improving security. In this paper, we propose an efficient wildcard searchable symmetric encryption with forward and backward security. The complexity of communication costs in the search protocol is O(1), independent of the number of characters of wildcard keywords, or files in the search result. It is achieved by a double-compressed index in which the character set and file identifiers are encoded simultaneously. Then, the double-compressed index provides a possibility to achieve oblivious keyword query and update, so that the proposed scheme only reveals the query type (search or update) without anything else. It achieves forward and backward security using the distributed multi-point function and an additively homomorphic symmetric encryption scheme. Detailed security proof and the theoretical comparison show the improvement in security and efficiency. The acceptable overheads of the proposed scheme are presented by the extensive performance evaluation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pattern matching

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Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare 18th

Genetic-A* Algorithm-Based Routing for Continuous-Flow Micr...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Huang, Huichang Yang, Zhongliao Zhong, Jiayuan Xu, Li Dong, Chen Bao, Ruishen College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microchannel networks into a single chip, enabling efficient execution of various biochemical protocols. However, as the scale of the chip increases, the routing task for CFMB becomes increasingly complex, and traditional manual routing is no longer sufficient to meet the requirements. Therefore, this paper proposes an automatic routing framework for CFMB based on Genetic algorithm (GA) and A* algorithms. Specifically, we adopt a two-stage A* algorithm to design the routing between modules, using the routing results obtained from the A* algorithm as the basis for evaluating the quality of solutions in the GA algorithm. Then, the GA algorithm is used to search for the optimal approximate solution in the solution space. Experimental results show that this method can reduce routing length and minimize routing crossings, thereby improving the parallel transmission speed of reagents on CFMB. This approach provides a feasible solution for large-scale automated routing of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic algorithms

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