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2023 4th International Conference on Electrical, Electronic information and Communication Engineering, EEICE 2023

作者： Zhu, Sheng Li, Weiwei Zhou, Cheng State Grid Smart Grid Research Institute Co. Ltd. Nanjing210003 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China

To solve the problem of link load imbalance and bandwidth waste in software defined security, a secure traffic scheduling method based on particle swarm optimization is proposed in this paper. This method combines the software defined security controller to obtain the network topology information and the current link bandwidth resource status, aiming at the matching degree between the link bandwidth and the network flow size and the function matching degree between the link node and the network flow service chain, combined with specific constraints to establish the objective function. The optimized particle swarm optimization algorithm finds out the best scheduling path of network traffic from the shortest path set. The experiment results show that compared with other algorithms, the proposed algorithm can improve the average throughput of the network and maintain a low packet loss rate in the process of traffic transmission, thus reducing the waste of bandwidth resources and realizing the load balancing of software defined network security traffic better. © Published under licence by IOP Publishing Ltd.

关键词： network security

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Lin Zou, Qingzhi Key Laboratory of Computing Power Network and Information Se-curity 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Due to the exceptional performance of Transform-ers in 2D medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%. © 2024 IEEE.

关键词： Image segmentation

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A verifiable privacy-preserving data collection scheme supporting multi-party computation in fog-based smart grid

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Frontiers of computer Science 2022年第1期16卷 143-153页

作者： Zhusen LIU Zhenfu CAO Xiaolei DONG Xiaopeng ZHAO Haiyong BAO Jiachen SHEN Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai200062China Cyberspace Security Research Center Peng Cheng LaboratoryShenzhen518055China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China

Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the ***,much attention has been paid to the research on smart grid,especially in protecting privacy and data ***,most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog *** this paper,we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC),named VPDC-MPC,to achieve both functions simultaneously in smart grid based on fog ***-MPC realizes verifiable secret sharing of users’data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing ***,we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’***,VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis(not only data aggregation)via secure multi-party computation between cloud servers in smart ***,VPDC-MPC tolerates fault of cloud servers and resists *** also present security analysis and performance evaluation of our scheme,which indicates that even with tradeoff on computation and communication overhead,VPDC-MPC is practical with above features.

关键词： smart grid fog computing data aggregation verifiable secret sharing error detection secure multi-party computation secure function query privacy-preserving

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An Efficient and Privacy-Preserving Data Aggregation Scheme Supporting Arbitrary Statistical Functions in IoT

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China Communications 2022年第6期19卷 91-104页

作者： Haihui Liu Jianwei Chen Liwei Lin Ayong Ye Chuan Huang College of Computer and Cyber Security Fujian Normal UniversityFuzhou 350117China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal UniversityFuzhou 350117China School of Computer Science and Mathematics Fujian University of TechnologyFuzhou 350118China Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhou 350117China

The Internet of Things(IoT)has profoundly impacted our lives and has greatly revolutionized our *** terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent ***,the high frequency of collecting user data will raise people concerns about personal *** recent years,many privacy-preserving data aggregation schemes have been ***,most existing schemes cannot support either arbitrary aggregation functions,or dynamic user group management,or fault *** this paper,we propose an efficient and privacy-preserving data aggregation *** the scheme,we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location *** this basis,the proposed scheme supports not only arbitrary aggregation functions,but also flexible dynamic user ***,the scheme achieves faulttolerant capabilities by utilizing a future data buffering *** analysis reveals that the scheme can achieve the desired security properties,and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.

关键词： Internet of Things data aggregation privacy protection arbitrary aggregation functions

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Dual Fusion network for Hyperspectral Semantic Segmentation 12th

Dual Fusion Network for Hyperspectral Semantic Segmentation

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12th International Conference on Image and Graphics, ICIG 2023

作者： Ding, Xuan Gu, Shuo Yang, Jian PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031463075

With the development of imaging technology, it becomes increasingly easy to obtain hyperspectral images (HSI) containing rich spectral information. The application of hyperspectral images in autonomous driving is expected to become a reality. Hyperspectral images have higher dimensions and more information than RGB images, which brings the challenge of training with limited samples. It is not easy to train satisfactory semantic segmentation models directly from hyperspectral images. In this paper, we propose a hyperspectral dual-fusion (hyperDF) network that can fully utilize the pre-training knowledge of the RGB modality to improve the performance of hyperspectral semantic segmentation. Specifically, we generate pseudo-color images from hyperspectral images to simulate RGB signals in order to make better use of the pre-trained RGB semantic segmentation models. The pseudo-color and hyperspectral images are processed in a dual encoder structure and then fused through a channel-wise attention fusion module. Finally, a multi-scale decoder injected with low-level features is used to predict the semantic segmentation results. Experimental results on the Hyperspectral City V2.0 dataset show that our method achieves state-of-the-art results, with a mIoU of 51.68%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantic Segmentation

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Cross-chain Transaction Tracking Protocol based on Multi-dimensional Digital Watermarking Fingerprints

Cross-chain Transaction Tracking Protocol based on Multi-dim...

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Frontiers Technology of information and computer (ICFTIC), IEEE International Conference on

作者： Duanzhen Li Shujiang Xu Haochen He Hongrui Xue 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 PR China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan PR China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Cross-chain technology plays a critical role in the process of interconnecting blockchains. However, all attention has been focused on how to achieve efficient cross-chain interoperability protocols, thus neglecting the most important security issues in cross-chain data interaction. Frequent network attacks and data privacy leaks have sounded the alarm for the safety of cross-chain data transactions. How to solve the problem of data ownership tracking in cross-chain transactions has become the top priority. This paper designs a cross-chain transaction tracking protocol based on multi-dimensional digital watermark fingerprints, using robust watermark, fragile watermark, and user identity key to achieve multi-dimensional tracking and supervision of cross-chain data transactions. Finally, this paper analyzes the feasibility and efficiency of the proposed protocol through experiments.

关键词： Data privacy Protocols Watermarking Fingerprint recognition Blockchains Safety Discrete wavelet transforms Interoperability

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Research on semantics and algorithm of formal analysis tool Scyther 4

Research on semantics and algorithm of formal analysis tool ...

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4th IEEE International Conference on Civil Aviation Safety and information Technology, ICCASIT 2022

作者： Xi, Chen Siqi, Lu Information Engineering University School of Cyberspace Security Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

Current security analysis on security protocols mainly relies on computational complexity and formalization. Based on the research of formal analysis method, this paper analyze the basic semantics, security attributes and verification algorithms of Scyther in detail, including the semantic derivation rules, the structure building of the security attribute and the realization mechanism of the algorithm of the tool, and systematically present the whole structure and technical details of the semantic and algorithm of the formal analysis tool. This paper lays a foundation for the optimization and design of subsequent formal analysis tools. © 2022 IEEE.

关键词： Semantics

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LateBA: Latent Backdoor Attack on Deep Bug Search via Infrequent Execution Codes 15

LateBA: Latent Backdoor Attack on Deep Bug Search via Infreq...

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15th Asia-Pacific Symposium on Internetware, Internetware 2024

作者： Yi, Xiaoyu Li, Gaolei Huang, Wenkai Lin, Xi Li, Jianhua Liu, Yuchen The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai Shanghai China The Department of Computer Science North Carolina State University Raleigh United States

ISBN: (纸本)9798400707056

Backdoor attacks can mislead deep bug search models by exploring model-sensitive assembly code, which can change alerts to benign results and cause buggy binaries to enter production environments. But assembly instructions have strict constraints and dependencies, and these additional model-sensitive assembly codes destroy semantics and syntax and are easily detected by dynamic analysis or context-based detection. To escape from the dynamic analysis-based detection, we propose a novel latent backdoor attack (LateBA) scheme based on the locality principle of program execution, which only poisons a few of infrequent execution codes, minimizing the effects on the original code logic. In LateBA, a progressive seed mutating strategy is designated to change the American Fuzzy Lop (AFL)-based path search tool to pay more attention to infrequent execution codes. With this strategy, the optimal range to positions in the whole program is determined. Subsequently, triggers are target model-sensitive assembly instructions, and try to minimize the variables that have been called in the context instructions in the trigger. Finally, we employ code semantic feature comparisons to select precise trigger injection positions within these ranges. The selection criteria of the trigger injection position is whether the corresponding code segments in this position have a data dependency relationship with other code segments. We evaluate the performance of LateBA over 7 deep bug search tasks. The results demonstrate the attack success rate of the proposed LateBA is considerable and competitive against the baselines. © 2024 Owner/Author.

关键词： Semantics

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Large-Scale Model Meets Federated Learning:A Hierarchical Hybrid Distributed Training Mechanism for Intelligent Intersection Large-Scale Model

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Big Data Mining and Analytics 2024年第4期7卷 1031-1049页

作者： Chang Liu Shaoyong Guo Fangfang Dang Xuesong Qiu Sujie Shao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Network Security Center State Grid Henan Electric Power Company Information Communication BranchZhengzhou 450052China

The large-scale model(LSM)can handle large-scale data and complex problems,effectively improving the intelligence level of urban ***,the traffic conditions at intersections are becoming increasingly complex,so the intelligent intersection LSMs(I2LSMs)also need to be continuously learned and *** traditional cloud-based training method incurs a significant amount of computational and storage overhead,and there is a risk of data *** combination of edge artificial intelligence and federated learning provides an efficient and highly privacy protected computing ***,we propose a hierarchical hybrid distributed training mechanism for ***,relying on the intelligent intersection system for cloud-network-terminal integration,we constructed an I2LSM hierarchical hybrid distributed training ***,we propose a hierarchical hybrid federated learning(H2Fed)algorithm that combines the advantages of centralized federated learning and decentralized federated ***,we propose an adaptive compressed sensing algorithm to reduce the communication ***,we analyze the convergence of the H2Fed *** results show that the H2Fed algorithm reduces the communication overhead by 21.6%while ensuring the accuracy of the model.

关键词： intelligent intersections large-scale models edge artificial intelligence(AI) federated learning compressed sensing

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Highway Gates Dynamic Adaptation network For Knowledge Graph Entity Alignment 21st

Highway Gates Dynamic Adaptation Network For Knowledge Graph...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Yusuf, Nursharbat Yilahun, Hankiz Imam, Seyyare Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi830017 China School of National Security Studies Xinjiang University Urumqi830017 China

ISBN: (纸本)9789819601240

The Entity alignment aims to align entities across different Knowledge Graphs and plays an important role in knowledge fusion tasks. In recent years, existing methods have typically used multiple aspects of information to enhance the accuracy of matching, which makes the matching process more comprehensive. However, existing methods that integrate multiple features have relied on fixed weighting strategies, making it challenging to adjust the relative importance of each feature. Therefore, this paper proposes an entity alignment framework(HGEA) based on highway gates dynamic adaptive network. Firstly, HGEA aggregates the structure, name, and node importance of entities. Among them, to fully utilize of the structural information of the Knowledge Graphs, we propose an entity and relation representation strategy based on a graph attention network. Then, we have a highway gates dynamic adaptation network, which combines the gating mechanism and multi-level self-attention to dynamically adjust the weight of each feature to adapt to the information flow of different entity features and optimize the entity alignment. Experiments on three real-world cross-lingual datasets demonstrate that our framework surpasses the current state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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