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arXiv 2021年

作者： Li, Zhen Tang, Jing Zou, Deqing Chen, Qian Xu, Shouhuai Zhang, Chao Li, Yichen Jin, Hai The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The University of Texas at San Antonio San AntonioTX78249 United States The University of Colorado Colorado Springs Colorado SpringsCO80918 United States Tsinghua University Beijing100084 China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention. In particular, deep learning-based vulnerability detectors, or DL-based detectors, are attractive because they do not need human experts to define features or patterns of vulnerabilities. However, such detectors' robustness is unclear. In this paper, we initiate the study in this aspect by demonstrating that DL-based detectors are not robust against simple code transformations, dubbed attacks in this paper, as these transformations may be leveraged for malicious purposes. As a first step towards making DL-based detectors robust against such attacks, we propose an innovative framework, dubbed ZigZag, which is centered at (i) decoupling feature learning and classifier learning and (ii) using a ZigZag-style strategy to iteratively refine them until they converge to robust features and robust classifiers. Experimental results show that the ZigZag framework can substantially improve the robustness of DL-based detectors. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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P-FedAvg: Parallelizing federated learning with theoretical guarantees 40

P-FedAvg: Parallelizing federated learning with theoretical ...

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40th IEEE Conference on Computer Communications, INFOCOM 2021

作者： Zhong, Zhicong Zhou, Yipeng Wu, Di Chen, Xu Chen, Min Li, Chao Sheng, Quan Z. Sun Yat-Sen University School of Computer Science and Engineering Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518000 China Department of Computing Macquarie University SydneyNSW2109 Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Tencent Technology Co. Ltd Shenzhen China

ISBN: (纸本)9780738112817

With the growth of participating clients, the centralized parameter server (PS) will seriously limit the scale and efficiency of Federated Learning (FL). A straightforward approach to scale up the FL system is to construct a Parallel FL (PFL) system with multiple PSes. However, it is unclear whether PFL can really achieve a faster convergence rate or not. Even if the answer is yes, it is non-trivial to design a highly efficient parameter average algorithm for a PFL system. In this paper, we propose a completely parallelizable FL algorithm called P-FedAvg under the PFL architecture. P-FedAvg extends the well-known FedAvg algorithm by allowing multiple PSes to cooperate and train a learning model together. In P-FedAvg, each PS is only responsible for a fraction of total clients, but PSes can mix model parameters in a dedicatedly designed way so that the FL model can well converge. Different from heuristic-based algorithms, P-FedAvg is with theoretical guarantees. To be rigorous, we conduct theoretical analysis on the convergence rate of P-FedAvg, and derive the optimal weights for each PS to mix parameters with its neighbors. We also examine how the overlay topology formed by PSes affects the convergence rate and robustness of a PFL system. Lastly, we perform extensive experiments with real datasets to verify our analysis and demonstrate that P-FedAvg can significantly improve convergence rates than traditional FedAvg and other competitive baselines. We believe that our work can help to lay a theoretical foundation for building more efficient PFL systems. © 2021 IEEE.

关键词： Network topology

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Secure Physical Layer Communications for Low-Altitude Economy Networking: A Survey

arXiv

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arXiv 2025年

作者： Cai, Lingyi Wang, Jiacheng Zhang, Ruichen Zhang, Yu Jiang, Tao Niyato, Dusit Wang, Xianbin Jamalipour, Abbas Shen, Xuemin Research Center of 6G Mobile Communications School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada School of Electrical and Computer Engineering University of Sydney Australia Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The Low-Altitude Economy Networking (LAENet) is emerging as a transformative paradigm that enables an integrated and sophisticated communication infrastructure to support aerial vehicles in carrying out a wide range of economic activities within low-altitude airspace. However, the physical layer communications in the LAENet face growing security threats due to inherent characteristics of aerial communication environments, such as signal broadcast nature and channel openness. These challenges highlight the urgent need for safeguarding communication confidentiality, availability, and integrity. In view of the above, this survey comprehensively reviews existing secure countermeasures for physical layer communication in the LAENet. We explore core methods focusing on anti-eavesdropping and authentication for ensuring communication confidentiality. Subsequently, availability-enhancing techniques are thoroughly discussed for anti-jamming and spoofing defense. Then, we review approaches for safeguarding integrity through anomaly detection and injection protection. Furthermore, we discuss future research directions, emphasizing energy-efficient physical layer security, multi-drone collaboration for secure communication, AI-driven security defense strategy, space-air-ground integrated security architecture, and 6G-enabled secure UAV communication. This survey may provide valuable references and new insights for researchers in the field of secure physical layer communication for the LAENet. Copyright © 2025, The Authors. All rights reserved.

关键词： Aircraft communication

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BED: A Block-Level Deduplication-Based Container Deployment Framework 15th

BED: A Block-Level Deduplication-Based Container Deployment ...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Zhang, Shiqiang Wu, Song Fan, Hao Zou, Deqing Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Scinece and Technology Wuhan430074 China

ISBN: (纸本)9783030642426

Container technology has gained great popularity in cloud environment since containers provide near-native performance and are lighter and less expensive than traditional virtual machines. However, starting up a non-local container, whose image is unavailable locally, is time-consuming. The reason is that pulling an image from a remote registry requires a long time. In this paper, we experimentally find that a lot of redundant data exists among image layers when we pull images from the registry. This redundant data causes additional pull time and makes the startup of a non-local container slower. To minimize the amount of pulled data while providing intact images, we propose a container deployment framework, BED, based on block-level deduplication. To be specific, BED stores an image layer as numerous data blocks and a fingerprint list which is generated based on these data blocks. When BED needs to pull an image, it pulls the fingerprint lists of the image, deduplicates these fingerprint lists, and pulls the data blocks non-existing locally from the registry. Based on the local and pulled blocks, BED reconstructs the image layers for a container. Experiments show that compared with original Docker, BED reduces the time of pulling images by 35% on average and saves about 48% data transmission in the network. © 2020, Springer Nature Switzerland AG.

关键词： Containers

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Segment-Level Cross-Modal Knowledge Transfer for Speech Sentiment Analysis 4

Segment-Level Cross-Modal Knowledge Transfer for Speech Sent...

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4th IEEE International Conference on Computer and Communication engineering technology, CCET 2021

作者： Zhang, Sheng Chen, Jincai Li, Minglei Li, Taisong Lu, Ping Wang, Zhu Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Huawei Cloud Bu Shenzhen518129 China Huawei Technology Co. Ltd. Shenzhen518129 China

ISBN: (纸本)9781665438902

Speech sentiments are inherently dynamic. However, most of the existing methods for speech sentiment analysis follow the paradigm: one single hard label for an entire utterance, which results in that segment-level fine-grained sentiment information is unavailable and sentiment dynamics can not be considered. In addition, due to the ambiguity of sentiments, it is difficult and time-consuming to annotate an utterance at the segment level. In this work, to alleviate the above issues, we propose to use sentiment profiles (SPs), which give a time series of the segment-level soft labels to capture the fine-grained sentiment cues across an utterance. To obtain a large number of labeled data with segment-level annotations, we propose a cross-modal knowledge transfer method at the segment level to transfer facial expression knowledge from images to audio segments. Further, we propose the sentiment profile refinery (SPR) to iteratively update sentiment profiles to improve the accuracy of SPs and overcome the noise problem in the cross-modal knowledge transfer method. Our experiments on the CH-SIMS dataset with the iQIYI-VID dataset as unlabeled data show that our method can effectively exploit additional unlabeled audio-visual data and achieve state-of-the-art performance. © 2021 IEEE.

关键词： Sentiment analysis

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A data Sharing Scheme in NDN Based on MOR Primitive

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International Journal of Network security 2022年第6期24卷 994-1001页

作者： Shi, Jiaoli Deng, Anyuan Luo, Chao Yao, Shimao He, Kai School of Computer and Big Data Science Jiujiang University Jiangxi Jiujiang China Institute of Information Security Jiujiang University Jiangxi Jiujiang China School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan China Hubei Clothing Information Engineering Technology Research Center Hubei Wuhan China

This work is aimed at a data sharing scheme over NDN (Named data Networking) with multiple demands: loosecouple, fine-grained control, and dynamic authority management. Firstly, a data access control scheme is proposed. Publisher generates data, encrypts the data to ciphertext, binds the ciphertext with an access policy, and publishes the ciphertext on an NDN router. Subscriber can access the part of data authorized when his private key meets the policy bound to the ciphertext, which realizes the loose-couple and fine-grained access control. The scheme can be constructed through Matrix computation, based on the MOR primitive instantiated by LWE. Secondly, a dynamic authority management method is presented. A subscriber’s latest identity public key is generated and stored on edge routers. When the subscriber applies to ciphertext, the edge routers attach the latest identity public key of the subscribers to the ciphertext and send it to the subscriber. This design can drive a subscriber to use his latest private key. © 2022, International Journal of Network security. All Rights Reserved.

关键词： Access control

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Multi-task representation learning with multi-view graph convolutional networks

arXiv

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arXiv 2021年

作者： Huang, Hong Song, Yu Wu, Yao Shi, Jia Xie, Xia Jin, Hai National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multi-task multi-view learning in this paper. We first explain the feasibility and advantages of multi-task multi-view learning for these two tasks. Then we propose a novel model named as MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multi-view graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network datasets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks. © 2021, CC BY.

关键词： data mining

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Grace: An Efficient Parallel SPARQL Query System over Large-Scale RDF data

Grace: An Efficient Parallel SPARQL Query System over Large-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiang Kang Yuying Zhao Pingpeng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

As a markup language for describing web resources, RDF is often used to represent graph data. SPARQL is a standard query language for RDF data, which is convenient in querying RDF. As RDF data grows rapidly, how to deal with complex queries over large-scale data in a reasonable time still remains many challenges. The existing RDF query systems often fail to respond within a reasonable time when dealing with complex SPARQL queries. Therefore, we propose an efficient parallel SPARQL query system and novel in three aspects. First, the proposed design provides a dynamic selectivity estimation strategy and generates an optimal query plan for parallel query processing. Second, the new design proposes a parallel processing model to maximize the parallelism of the system. Finally, chunk-based task distribution strategy is implemented to assist the parallel processing model. Based on the proposed design, we implement an efficient parallel query system (Grace). Extensive experiments on LUBM and BTC benchmarks show that Grace outperforms RDF-3X, Virtuoso, TripleBit and achieves a good performance of scalability on the number of threads.

关键词： Distribution strategy Scalability Query processing Markup languages Instruction sets Estimation Parallel processing

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Hardware-Supported Remote Persistence for Distributed Persistent Memory

Hardware-Supported Remote Persistence for Distributed Persis...

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Supercomputing Conference

作者： Zhuohui Duan Haodi Lu Haikun Liu Xiaofei Liao Hai Jin Yu Zhang Song Wu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

The advent of Persistent Memory (PM) necessitates an evolution of Remote Direct Memory Access (RDMA) technologies for supporting remote data persistence. Previous software-based solutions require remote CPU intervention and postpone the visibility of remote persistence. In this paper, we design several hardware-supported RDMA primitives to flush data from the volatile cache of RDMA Network Interface Cards (RNICs) to the PM. We also propose durable RPCs based on the proposed RDMA Flush primitives to support remote data persistence and fast failure recovery. We emulate the performance of RDMA Flush primitives through other RDMA prim-itives, and compare our proposals with several state-of-the-art RPCs in a real testbed equipped with PM and InfiniBand networks. Experimental results show that our proposals can improve the throughput of RPCs by up to 90%, and reduce the 99th percentile latency by up to 49%. The experimental studies also provide instructive guidelines for designing RDMA-based distributed PM systems.

关键词： High performance computing Throughput Proposals Network interfaces Guidelines

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Heterogeneous Parallel Computing Based Real-Time Chaotic Video Encryption and its Application to Drone-Oriented Secure Communication

SSRN

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SSRN 2023年

作者： Jiang, Dong Shi, Fan-Feng Li, Tao Li, Yi-Fei Shan, Dan School of Information Engineering Yangzhou Polytechnic Institute Yangzhou225007 China School of Internet Anhui University Hefei230039 China College of Physical Science and Technology Yangzhou University Yangzhou225009 China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui University Hefei230601 China

This paper proposes a real-time video encryption protocol based on multi-round confusion-diffusion architecture and heterogeneous parallel computing. It leverages the powerful computing capacity of Central Processing Unit (CPU) and the high parallel capability of Graphics Processing Unit (GPU) to perform byte generation, confusion and diffusion operations concurrently, thereby improving computational efficiency. Statistical and security analysis show that the proposed protocol exhibits exceptional statistical properties and provides resistance against different types of attacks. Encryption speed evaluation demonstrates that it can realize latency-free 640x640 30FPS video encryption using Intel Core i7-8700 and NVIDIA GeForce GTX 1060, with an average encryption time of 20.43ms, despite performing seven rounds of confusion and three rounds of diffusion operations on each frame. Additionally, the proposed protocol is adopted to implement a drone-oriented secure video communication system, with NVIDIA Jetson Xavier NX (NVIDIA Camel ARM CPU and Volta GPU), achieving latency-free 256x256 29FPS video encryption. © 2023, The Authors. All rights reserved.

关键词： Program processors

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