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11th International Conference on computer Engineering and networks, CENet2021

作者： Wang, Zhiqiang Wang, Ziyi Wang, Zhuoyue Zhang, Zhirui Yang, Tao Beijing Electronic Science and Technology Institute Beijing100070 China State Information Center Beijing100045 China Key Lab of Information Network Security Ministry of Public Security Shanghai China

ISBN: (纸本)9789811665530

network attacks affect the security of basic network facilities and critical information systems and cause serious damage to the national security. It is vital and important to establish a platform of software and hardware security vulnerability for collecting, verifying, sharing vulnerabilities. Aiming at the problems of low automation, high labor cost in the current domestic and foreign vulnerability sharing platform, this paper designs and implements a security vulnerability sharing platform based on a web crawler. The crawler technology is used to crawling the vulnerabilities’ data automatically. Moreover, more comprehensive vulnerability information query and sharing are realized, which is convenient for early warning and repair of security vulnerabilities. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： network security

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Automatic Classification and Recognition of Seismic Waveforms Based on Convolutional Neural networks 2

Automatic Classification and Recognition of Seismic Waveform...

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2nd IEEE International Conference on networks, Multimedia and information Technology, NMITCON 2024

作者： Shan, Juhao Huang, Hanming Ministry of Education Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security China Guangxi Normal University School of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350372892

The development of efficient, high-precision, and universally applicable automatic waveform picking algorithms is becoming increasingly important in the era of seismic big data. The main challenge of waveform automatic picking algorithm lies in how to adapt to the classification and screening of different types of seismic events in different regions. Traditional seismic phase identification relies on the theoretical knowledge and experience of relevant researchers, which has problems such as large workload, long interpretation time, strong subjectivity, and low efficiency. Deep learning technology can learn the hidden features in data. This article studies how to use deep convolutional neural networks to learn the nonlinear mapping relationship between seismic data and seismic phases, providing technical support for achieving automated seismic phase recognition. This article mainly focuses on the automatic classification and recognition of seismic waveforms using convolutional neural networks. Firstly, relevant research is summarized, relevant application methods are proposed, and the results are discussed to provide assistance for the development of related fields. © 2024 IEEE.

关键词： Convolutional neural networks

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Community search over heterogeneous information networks via weighting strategy and query replacement

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Frontiers of computer Science 2022年第4期16卷 231-233页

作者： Fanyi Yang Huifang Ma Weiwei Gao Zhixin Li College of Computer Science and Engineering Northwest Normal UniversityLanzhou730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin541004China

1Introduction and main contributions Recently,community search over Heterogeneous information networks(HINs)has attracted much attention in graph analysis,which aims to search for local communities containing query *** existing community search studies in HINs have proved effective in converting heterogeneous graphs to homogeneous graphs via pre-defined meta-paths with consistent head and tail node types,two major limitations ***,they fail to properly utilize the intermediate nodes to assign weights on the edges of the induced homogeneous graph.

关键词： utilize weights properly

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Maximizing Area Coverage in Privacy-Preserving Worker Recruitment: A Prior Knowledge-Enhanced Geo-Indistinguishable Approach

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IEEE Transactions on information Forensics and security 2025年 20卷 5138-5151页

作者： Zhang, Pengfei Cheng, Xiang Zhang, Zhikun Zhu, Youwen Zhang, Ji Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Yunnan Kunming650221 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250316 China Ministry of Education Key Laboratory of Equipment Data Security and Guarantee Technology Guilin541004 China Beijing University of Posts and Telecommunications Beijing100876 China Zhejiang University School of Computer Science and Technology Hangzhou310027 China Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nanjing210016 China University of Southern Queensland School of Mathematics Physics and Computing ToowoombaQLD4350 Australia

Worker recruitment for area coverage maximization, typically requires participants to upload location information, which can deter potential participation without proper protection. While existing studies resort to geo-indistinguishability to address this concern, they primarily focus on either specific task locations (Target Coverage) or operate under pre-defined recruitment quotas for an interested region (Area Coverage). These focuses not only yield suboptimal area coverage when scaled but also fail to leverage valuable prior knowledge in the form of participants' noisy historical registered locations, to enhance both location obfuscation and worker identification processes. To address these limitations, we present WILTON, which optimizes area coverage under geo-indistinguishability by recruiting the minimum number of participants through the strategic utilization of noisy prior knowledge. In WILTON, to generate obfuscated locations, we propose a probabilistic and weight-aware input perturbation mechanism, which groups and weights prior locations rather than using only personal prior locations. To privately identify the recruited workers, we design a grid-based worker identification method, which provides a worst-case performance guarantee of ratio 1 -1/e to the optimum. We provide a theoretical analysis of the privacy, utility, and complexity guarantees of WILTON. Experimental results over two real-world datasets and one synthetic dataset show that WILTON surpasses the state-of-the-arts by at least 8% in area coverage improvement. © 2005-2012 IEEE.

关键词： Knowledge acquisition

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Visual malware classification and detection method based on convolutional neural network 9

Visual malware classification and detection method based on ...

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

作者： Huang, Hua Sheng, Hua Han, Xingwang Zhang, Wenbin Zhao, Zilin State Grid Shandong Electric Power Company Information and Telecommunications Company 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

ISBN: (纸本)9798350391367

With the rapid development of Internet technology, leading to the evolution of malware variants, and malicious threats are also increasing. Therefore, rapid and accurate classification of malware becomes crucial. In response to this challenge, this paper proposes a malware classification method based on deep learning and visualization techniques. The method first extracts sample features, then converts malware binary samples into image format, thus transforming abstract binary data into images with visual information. Subsequently, color mapping technique is applied to convert the processed images into color images. Furthermore, to address imbalanced datasets, this paper introduces data augmentation methods to ensure more balanced and comprehensive training of the model across different sample categories. Finally, an improved convolutional neural network architecture is employed for the classification detection of malware families, achieving accurate classification and identification of different malware families through learning and analysis of sample features. To evaluate the effectiveness of the proposed method, experiments are conducted on the Google Code Jam (GCJ) benchmark dataset. The experimental results demonstrate an accuracy rate of 99.29% on the GCJ dataset. Additionally, we also compared the performance of the improved method before and after, and found that the proposed method can provide more comprehensive feature representation and significantly improve classification accuracy and detection efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Convertible certificateless proxy re-signcryption for data access control in cloud computing

Convertible certificateless proxy re-signcryption for data a...

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2023 International Conference on computer Application and information security, ICCAIS 2023

作者： Wu, Chenhuang Yang, Linghui Yuan, Beibei Li, Huimin Huang, Hui Fujian Key Laboratory of Financial Information Processing Putian University Fujian Putian351100 China Fujian Zhongxin Network Security Information Technology Co. Ltd. Fujian Fuzhou350011 China School of Computer Science Minnan Normal University Fujian Zhangzhou363000 China

ISBN: (纸本)9781510675032

Cloud storage service enables users not only to save their local data storage costs but also to access their outsourced data at any time and anywhere. Traditionally, a data owner outsources the encrypted data to the cloud server and then checks its integrity with the help of cloud auditing strategies. Obviously, in such a manner, only confidentiality and integrity are considered following the above two independent steps. Furthermore, it becomes an issue that the data owner may want to share their outsourced data with some authorized data receivers and convince them that the data are from and the same as the data owner's. To solve this issue, absorbing the advantage of the certificateless cryptosystem, we propose an efficient certificateless proxy re-signcryption (CLPRSC) for data access control in cloud computing. Moreover, the CLPRSC possesses ciphertext verifiable, convertible, and designated re-signcryptable properties with the highest efficiency among the achievable signcryption with proxy re-encryption schemes, which may be of independent interest. © 2024 SPIE.

关键词： Access control

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A two-path fiber-optic AE demodulation algorithm based on Numerically Stable Direct Least Squares Fitting of Ellipses

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Journal of Lightwave Technology 2025年

作者： Li, Xinhao Zhang, Faxiang Sun, Zhihui Jiang, Shaodong Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

The 3 × 3 coupler demodulation algorithm commonly employs ellipse fitting algorithms (EFAs) to eliminate coupler asymmetry. In the detection scenario of fiber-optic acoustic emission (AE) sensor arrays, the amount of data increases significantly due to the high sampling rate and multi-channel characteristics. Traditional EFAs require substantial computational resources and involve complex calculations, which severely hinder demodulation efficiency. To address these issues, We propose a two-path demodulation algorithm based on Numerically Stable Direct Least Squares Fitting of Ellipses (NSDLS). The algorithm only two input signals and divides the fitting process into linear and quadratic components, reducing computational complexity. The triggered ellipse fitting judgment method is introduced to mitigate background noise impact on fitting results and enhance demodulation efficiency. Experimental results demonstrate that the proposed two-path demodulation algorithm based on NSDLS reduces time consumption by approximately 40 times and the CPU utilization by 40% compared with traditional EFAs demodulation algorithm, improves SINAD by 18.93 dB compared with the improved Naval Postgraduate School (NPS) method. The algorithm achieves a dynamic range of 126.04 dB @ 1 kHz, 0.075% THD, and 99.999% response linearity. This method maintains high demodulation accuracy while significantly reducing computational resource demands, lowering hardware costs, and improving overall demodulation efficiency. © 1983-2012 IEEE.

关键词： Acoustic emission testing

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A Fast recognition method for DVS perimeter security intrusion signals based on transfer learning

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Optical Fiber Technology 2025年 93卷

作者： Shen, Yongxu Wang, Chang Sun, Zhihui Zhang, Faxiang Jiang, Shaodong Liu, Xu Gao, Fengxia Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

In the perimeter security intrusion signal monitoring based on the Distributed Optical Fiber Vibration Sensing (DVS) system, the common approach for identifying intrusion signals in new scenarios is to collect large amounts of data and perform large-scale, long-term retraining of the initial network. However, this process consumes a significant amount of human resources and computational power each time. To address this issue, this paper proposes a transfer learning method. Using 53,000 sample data from six types of events collected in the laboratory, an SE-Densenet model was trained for 300 epochs on three NVIDIA A100 Tensor Core GPUs at the National Supercomputing Center in Jinan. Then, through the transfer learning approach, fine-tuning was performed on the new scenario by freezing 1/2 and 1/4 of the network layers and applying a feature extractor method. The fine-tuning training was carried out on a laptop equipped with 16 GB of memory and an Intel Core i7-12700 CPU. Experimental results show that, after 19.81 min of retraining using 600 data points from the new scenario, the feature extractor method achieved a test accuracy of 97.53 %. Compared to the other two methods, the training time was reduced by 20.87 min and 9.34 min, respectively, while the test accuracy increased by 13.76 % and 3.83 %. Compared to retraining with 6000 sufficient data, the transfer learning method improved training time by 6 to 13 times while achieving similar recognition accuracy. This study provides a new, convenient, and efficient method for perimeter security intrusion signal recognition, enabling quick adaptation to new scenarios and application in common engineering upper-level computer systems. © 2025

关键词： network intrusion

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Fault-tolerant identity-based encryption from SM9

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Science China(information Sciences) 2024年第2期67卷 104-117页

作者： Xiaohong LIU Xinyi HUANG Zhaohui CHENG Wei WU Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal University Artificial Intelligence Thrust Information HubThe Hong Kong University of Science and Technology (Guangzhou) Olym Information Security Technology Ltd. Center for Applied Mathematics of Fujian Province School of Mathematics and StatisticsFujian Normal University

This paper initiates the formal study of attribute-based encryption within the framework of SM9,the Chinese National Cryptography Standard for Identity-Based Cryptography, by presenting two new faulttolerant identity-based encryption(FIBE) schemes. Our first scheme uses the same private-key/ciphertext structure as the original SM9 algorithm and operates in a small attribute universe. As a result, it can be effectively and smoothly integrated into the information systems using SM9. In the random oracle model,we prove that our scheme is ciphertext-indistinguishable against fuzzy selective-identity and chosen-plaintext attacks under the(k + 3)-DBDHI assumption. Our second design is a large universe FIBE scheme based on SM9 that is ciphertext-indistinguishable against chosen-plaintext attacks in the random oracle model under the(f, g)-GDDHE assumption. Finally, we compare the communication and computing costs of our schemes to those of other classical ones. The comparison shows that our schemes have comparable performance as others. We believe that our findings will accelerate the applications of SM9 in modern information systems such as cloud computing and blockchain.

关键词： attribute-based encryption identity-based encryption fault-tolerant SM9

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