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

作者： Wang, Lianhai Wang, Xinlei Liu, Tianrui Wang, Yingxiaochun Li, Qi 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 Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

The rapid growth of healthcare data transactions has fostered win-win developments in healthcare technology innovation and economic benefits. However, the issue of secondary transactions makes data ownership vulnerable to infringement due to the easily replicable nature of the data. As the importance of ownership and privacy increases, more and more users protect the privacy of data through encryption methods, making it impossible for existing methods to make infringement judgments on ciphertext data. To solve this problem, we propose an infringement detection and traceability scheme based on feature extraction of ciphertext data. Firstly, the characteristics of fully homomorphic encryption are utilized to obtain data features in the ciphertext state, and the plaintext state of these features is used as the basis for infringement judgment. Additionally, during the data transaction process, the ownership registration of the sales data is verified through smart contracts to ensure the effective association between the transaction data and the seller's address. The experimental results show that the scheme can accurately determine infringement while protecting data security and privacy. © 2024 IEEE.

关键词： Differential privacy

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Minimum Path Cost Multi-path Routing Algorithm with No Intersecting Links in Quantum key Distribution networks 6

Minimum Path Cost Multi-path Routing Algorithm with No Inter...

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2023 IEEE 6th International Conference on Information Systems and computer Aided Education, ICISCAE 2023

作者： Miao, Minghui Fang, Shuo Wu, Weijie Yuan, Xiaotong Bi, Lin Key Laboratory of Network and Information Security in Jilin Province Changchun China Changchun University of Science and Technology School of Computer Science and Technology Jilin Changchun China

ISBN: (纸本)9798350313444

Quantum key distribution (QKD) provides information-theoretic security based on quantum mechanics, and as quantum key distribution becomes increasingly practical, questions of how to employ it effectively over largescale networks and large distances becomes increasingly important. In this work, we investigate the B92 entanglement based QKD protocol and a five-layer QKD network architecture consisting of trusted relay nodes. Previous research seldom focused on the ground routing mechanism of QKD networks in detail. Since no intersecting links are beneficial to reducing the key consumption rate and improving link security, combined with the core idea of reducing the number of link relay hops, we propose a minimum path cost multi-path routing algorithm for no intersecting links in quantum key distribution networks. By analyzing the security of the link, we effectively reduce the number of relay nodes and the number of relay links that pass through and give priority to the routing scheme with the minimum number of hops in the number of relay hops, which avoids the congestion and waste caused by the existence of interleaved multi-path in traditional multi-path routing. Meanwhile, the scheme effectively improves the quality of service index, avoids the generation and consumption of redundant keys, and reduces the waste of quantum key resources. © 2023 IEEE.

关键词： quality of service quantum key distribution quantum network routing algorithm

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MSPL: Multi-modal Statistical Prompt Learning for New Energy Equipment Defect Recognition

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IEEE Internet of Things Journal 2025年第11期12卷 16438-16450页

作者： Wang, Zhenwei Wang, Pengfei Han, Guangjie Zhou, Chengjie Lin, Qing Zhang, Jianxin Fan, Guangjie Zhang, Qiang Dalian University of Technology School of Computer Science and Technology Dalian116024 China Dalian116024 China Shenyang Huayan Electric Technology Co Ltd Shenyang110180 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China Deputy Director of the Yantai Big Data Center and Senior Engineer Yantai264003 China Dalian Minzu University School of Computer Science and Engineering Dalian116600 China

Monitoring renewable energy devices is crucial for the timely detection of faults and the improvement of system stability, making it a key component of the Internet of Things (IoT) ecosystem. However, existing intelligent algorithms and IoT methods rely on edge devices with limited computational capacity, which requires balancing real-time performance and accuracy. Additionally, most methods require initial training on large-scale data using cloud platforms or high-performance servers, leading to high initial costs. To address these challenges, we propose Multi-modal Statistical Prompt Learning (MSPL) for new energy equipment defect recognition on IoT edge devices. This method enables rapid learning with only a few samples, avoiding the need for large-scale centralized training. Specifically, MSPL uses the pre-trained Contrastive Language-Image Pre-training model as its backbone, leveraging text-based conceptual information to enhance the understanding of visual inputs. A Statistical Query (SQ) module is implemented at the end of the backbone to extract distinctive features from the outputs, integrating these features with soft prompts to customize them for the defect recognition task. Since the learnable parameters are limited to soft prompts added at the end of the backbone, MSPL restricts gradient backpropagation to this point. This improves parameter and memory efficiency, making it more suitable for scenarios with limited computational capacity on IoT edge devices. Experimental results of MSPL on two renewable energy equipment defect datasets and edge devices indicate that it meets real-time processing requirements while maintaining high accuracy, outperforming other methods. © 2025 IEEE.

关键词： Zero-shot learning

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Rail Transit Event Extraction based on the Fusion of Semantic and Local Dependency Features 15

Rail Transit Event Extraction based on the Fusion of Semanti...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Chen, Qiang Meng, Haining Bao, Yihao Yang, Mingsong Tong, Xinyu Zhao, Qin Khan, Muhammad Bilal Hei, Xinhong Xi'an University of Technology School of Computer Science and Engineering Xi'an China Xi'an University of Technology School of Computer Science and Engineering Shaanxi Key Laboratory of Network Computing and Security Technology Xi'an China Xi'an University of Technology School of Civil Engineering and Architecture Xi'an China

ISBN: (纸本)9798331508821

Event extraction in rail transit plays a vital role in all stages of design, construction, and final acceptance. Event extraction is a key part of building event logic graph (ELG). However, the difficulty in obtaining semantic features and event local dependencies poses a challenge to accurate event extraction, which hinders the informatization and intelligent development of rail transit projects. To address these challenges, we proposes an event extraction method that fuses semantic and local dependency features. The study initially preprocesses the rail transit design codes dataset and performs sequence labeling. Then, the event trigger words are mined by combining the term frequency-inverse document frequency (TF-IDF) and minimum edit distance (MED) algorithms. Based on the semantic similarity measurement method and the trigger word mining results, the features representation of event semantic information is enhanced by using the Bidirectional Encoder Representations from Transformers (BERT) model and Bi-directional Long Short-Term Memory (BiLSTM) model. At the same time, event arguments are mined and a local dependency graph is generated based on their mining results, and then the enhanced local dependency features are obtained by using the graph convolutional network (GCN). Finally, the event semantic features and local dependency features are fused, and the conditional random field (CRF) is used to predict the labels to obtain the event extraction results. Through the comparison with five baseline methods on the datasets of rail transit design codes, it is found that the Precision, Recall, and F1 score of the proposed method are improved, with an average improvement of 9.604% on Precision, 9.342% on Recall, and 9.730% on F1 score, respectively. The contribution of the sub-modules to the overall performance is further evaluated through ablation experiments, which confirms the effectiveness and feasibility of the proposed method. © 2024 IEEE.

关键词： Semantics

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Achieving reliable and anti-collusive outsourcing computation and verification based on blockchain in 5G-enabled IoT

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Digital Communications and networks 2022年第5期8卷 644-653页

作者： Linjie Wang Youliang Tian Jinbo Xiong School of Data Science Tongren UniversityTongren554300China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou UniversityGuiyang550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things(IoT)cloud to improve transmission efficiency,which has created a novel paradigm for improving the speed of common connected objects in ***,although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services,security concerns,such as a lack of reliability and collusion attacks,still exist in the outsourcing *** this paper,we propose a reliable,anti-collusion outsourcing computation and verification protocol,which uses distributed storage solutions in response to the issue of centralized storage,leverages homomorphic encryption to deal with outsourcing computation and ensures data ***,we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum,which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion *** results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure,reliable,and more effective compared with state-of-the-art approaches.

关键词： Outsourcing computation Ethereum Smart contract Public verification Homomorphic encryption

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A novel firmware secure transmission scheme based on SM2 threshold key agreement algorithm

A novel firmware secure transmission scheme based on SM2 thr...

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2023 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Wang, Kun Gao, Shaofeng Chen, Xinze Fu, Yong 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: (纸本)9781510677685

During the process of embedded firmware upgrade, to protect the security of the firmware during transmission, this paper proposes an improved firmware transmission scheme based on the combination of the SM2 threshold key agreement algorithm. Between the server, client, and downloader, the SM2 key agreement algorithm is used to generate session key pairs to encrypt the firmware information, and the method of reinforcing and verifying re-transmission is adopted to ensure data security. In order to improve the security of the algorithm, a threshold algorithm is used in the key agreement process. The server acts as a trusted center to execute the threshold algorithm and divide the main secret private keys dA and dB. During the phase of key exchange, at least t+1 sub-secrets are required to recover the complete main secret. This paper successfully implements and verifies the feasibility of this scheme. © 2024 SPIE.

关键词： Firmware

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Distributed acoustic sensing signal event recognition and localization based on improved YOLOv7

Distributed acoustic sensing signal event recognition and lo...

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2023 International Congress on Ultrasonics, ICU Beijing 2023

作者： Zhang, Hongyu Wang, Chang Zhang, Faxiang Jiang, Shaodong Sun, Zhihui Wang, Xiaodong Duan, Zhenhui Gao, Fengxia Liu, Zhaoying 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

The distributed acoustic sensing (DAS) system based on phase-sensitive optical time domain reflection (Φ -OTDR) technology is widely used in pipeline safety monitoring, perimeter security, structure monitoring, etc. Accurate localization and recognition of multiscene events over long distances has always been a challenge. This paper proposes an improved YOLOv7 algorithm for multi-event real-time detection of DAS system. The algorithm employs space-to-depth Conv(SPD-Conv) to replace the strided convolutions and pooling operations in YOLOv7, reducing fine-grained information loss and learning of inefficient feature representations. In addition, the Convolutional Block Attention Module (CBAM) is introduced in YOLOv7 to improve the model performance. We collected spatial-temporal signal data for six types of pipeline safety events, and passed them into the improved YOLOv7 algorithm in the form of data matrixes for training and *** have shown that the proposed method achieves an mAP@.5 (mean Average Precision) of 99.7% for the identification of six pipeline safety event types. Positioning loss reduced to 0.2%,and detection speed can reach 70 frames per second(FPS). Our scheme achieves significant improvements in localization and classification accuracy compared to Faster R-CNN, etc. The event recognition localization method proposed in this paper has the advantage of fast speed and high accuracy. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Light reflection

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Lightweight Malicious Encrypted Traffic Detection Method Based on KL Image Compression 9

Lightweight Malicious Encrypted Traffic Detection Method Bas...

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

作者： Wen, Yuecheng Li, Xiaotong Lv, Haiqing Wang, Menglu Zuo, Wenbo 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

Timely and accurate detection of and response to potentially malicious encrypted traffic is crucial in cybersecurity strategies. Convolutional neural network-based approaches that convert traffic loads into images for analysis have made significant progress in malicious encrypted traffic detection, but they suffer from the challenges of large number of participants, large network size, and high demand for computational resources, especially when edge computing devices are limited. Therefore, there is an urgent need for a lightweight, efficient and accurate malicious encrypted traffic detection model. This paper proposes a malicious encrypted traffic detection model whose core idea is to utilize KL image compression technique. The model first converts the raw traffic data into grayscale images and applies KL transform to compress the images efficiently. Subsequently, a convolutional neural network model with lightweight design is used to classify the compressed image for fast and accurate malicious encrypted traffic detection. After validating on both the public traffic dataset and the lab's self-collected dataset, the results indicate that our method requires just 0.0216 seconds to process a single input sample, achieving an average classification accuracy of 98%. These findings highlight its practicality and excellence in addressing the real-world challenges of malicious encrypted traffic. © 2024 IEEE.

关键词： Cybersecurity

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