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Opportunistic Wiretapping/Jamming: A New Attack Model in Millimeter-Wave Wireless networks

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TechRxiv

TechRxiv 2022年

作者： Zhang, Yuanyu Zheng, Zhumeng He, Ji Zhao, Shuangrui Qu, Qianyue Shen, Yulong Jiang, Xiaohong School of Computer Science and Technology Xidian University Shaanxi Xi’an China Shannxi Key Laboratory of Network and System Security China The Beijing Sunwise Information Technology Ltd Beijing Institute of Control Engineering Beijing China The Division of Information Science Graduate School of Science and Technology Nara Institute of Science and Technology Nara Ikoma Japan School of Systems Information Science Future University Hakodate Hokkaido Hakodate Japan

While the millimeter-wave (mmWave) communication is robust against the conventional wiretapping attack due to its short transmission range and directivity, this paper proposes a new opportunistic wiretapping and jamming (OWJ) attack model in mmWave wireless networks. With OWJ, an eavesdropper can opportunistically conduct wiretapping or jamming to initiate a more hazardous attack based on the instantaneous costs of wiretapping and jamming. We also provide three realizations of the OWJ attack, which are mainly determined by the cost models relevant to distance, path loss and received power, respectively. To understand the impact of the new attack on mmWave network security, we first develop novel approximation techniques to characterize the irregular distributions of wiretappers, jammers and interferers under three OWJ realizations. With the help of the results of node distributions, we then derive analytical expressions for the secrecy transmission capacity to depict the network security performance under OWJ. Finally, we provide extensive numerical results to illustrate the effect of OWJ and to demonstrate that the new attack can more significantly degrade the network security performance than the pure wiretapping or jamming attack. © 2022, CC BY.

关键词： Millimeter waves

Opportunistic Wiretapping/Jamming: A New Attack Model in Millimeter-Wave Wireless networks

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arXiv 2022年

作者： Zhang, Yuanyu Zheng, Zhumeng He, Ji Zhao, Shuangrui Qu, Qianyue Shen, Yulong Jiang, Xiaohong School of Computer Science and Technology Xidian University Shaanxi Xi’an China Shannxi Key Laboratory of Network and System Security China Beijing Sunwise Information Technology Ltd Beijing Institute of Control Engineering Beijing China Division of Information Science Graduate School of Science and Technology Nara Institute of Science and Technology Nara Ikoma Japan School of Systems Information Science Future University Hakodate Hokkaido Hakodate Japan

关键词： Millimeter waves

Corrigendum to“Achievements,challenges,and recommendations for waterbird conservation in China's coastal wetlands”[Avian Res.14(2023)100123]

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Avian Research 2023年第3期14卷 315-316页

作者： Zhijun Ma Chi-Yeung Choi Xiaojing Gan Jing Li Yang Liu David S.Melville Tong Mu Theunis Piersma Zhengwang Zhang Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering National Observation and Research Station for Wetland Ecosystems of the Yangtze EstuaryFudan UniversityShanghai200438China Division of Natural and Applied Sciences Environmental Research CentreDuke Kunshan UniversityKunshan215316JiangsuChina The Paulson Institute Beijing Representative Office Beijing100006China Spoon-Billed Sandpiper Shanghai Environmental Protection Technology Co.Ltd.Shanghai201100China State Key Laboratory of Biocontrol School of EcologySun Yat-Sen UniversityGuangzhou510275China Global Flyway Network Wakefield7096New Zealand Princeton School of Public and International Affairs Princeton UniversityPrincetonNJ08544USA NIOZ Royal Netherlands Institute for Sea Research Department of Coastal SystemsP.O.Box 591790 ABDen BurgTexelthe Netherlands BirdEyes Centre for Global Ecological Changeand Conservation Ecology GroupGroningen Institute for Evolutionary Life Sciences(GELIFES)University of Groningen9700 CCGroningenthe Netherlands CEAAF Center for East Asian-Australasian Flyway Studies Beijing Forestry UniversityQinghua East Road 35Haidian DistrictBeijing100083China Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering School of Life SciencesBeijing Normal UniversityBeijing100875China

The authors regret that Fig.1 and its caption should be replaced as below.

关键词： coastal conservation challenges

Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

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SSRN

SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Segment Anything Model is a Good Teacher for Local Feature Learning

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arXiv 2023年

作者： Wu, Jingqian Xu, Rongtao Wood-Doughty, Zach Wang, Changwei Xu, Shibiao Lam, Edmund Y. The University of Hong Kong Pokfulam Hong Kong The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China Northwestern University EvanstonIL60201 United States The 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 Jinan250013 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in any scene and any downstream task. Data-driven local feature learning methods need to rely on pixel-level correspondence for training. However, a vast number of existing approaches ignored the semantic information on which humans rely to describe image pixels. In addition, it is not feasible to enhance generic scene keypoints detection and description simply by using traditional common semantic segmentation models because they can only recognize a limited number of coarse-grained object classes. In this paper, we propose SAMFeat to introduce SAM (segment anything model), a foundation model trained on 11 million images, as a teacher to guide local feature learning. SAMFeat learns additional semantic information brought by SAM and thus is inspired by higher performance even with limited training samples. To do so, first, we construct an auxiliary task of Attention-weighted Semantic Relation Distillation (ASRD), which adaptively distillates feature relations with category-agnostic semantic information learned by the SAM encoder into a local feature learning network, to improve local feature description using semantic discrimination. Second, we develop a technique called Weakly Supervised Contrastive Learning Based on Semantic Grouping (WSC), which utilizes semantic groupings derived from SAM as weakly supervised signals, to optimize the metric space of local descriptors. Third, we design an Edge Attention Guidance (EAG) to further improve the accuracy of local feature detection and description by prompting the network to pay more attention to the edge region guided by SAM. SAMFeat’s performance on various tasks such as image matching on HPatches, and long-term visual localization on Aachen Day-Night showcases its superiority over previous local features. The release code is available at https://***/vignywang/SAMFeat

关键词： Computer vision

Efficient quantum state transmission via perfect quantum network coding

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Science China(Information Sciences) 2019年第1期62卷 191-204页

作者： Zhen-Zhen LI Gang XU Xiu-Bo CHEN Zhiguo QU Xin-Xin NIU Yi-Xian YANG Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology

Quantum network coding with the assistance of auxiliary resources can achieve perfect transmission of the quantum state. This paper suggests a novel perfect network coding scheme to efficiently solve the quantum k-pair problem, in which only a few assisting resources are introduced. Specifically, only one pair of maximally entangled state needs to be pre-shared between two intermediate nodes, and only O(k) of classical information is transmitted though the network. Moreover, the classical communication used in our protocol does not cause transmission congestion, providing better adaptability to large-scale quantum k-pair *** relevant analyses and comparisons, we demonstrate that our proposed scheme saves resources and has good application value, thereby showing its high efficiency. Furthermore, the proposed scheme achieves 1-max flow quantum communication, and the achievable rate region result is extended from its counterpart over the butterfly network.

关键词： perfect quantum network coding quantum k-pair problem efficient quantum state transmission communication efficiency achievable rate region

A 2-bit graphene encoder based on the plasmon-induced transparency effect and its sensing characteristics

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Results in Physics 2024年 59卷

作者： Zhu, Aijun Li, Zongteng Hou, Weigang Yang, Xinghua Cheng, Lei Hu, Cong Qiao, Fei Mahapatra, Rabi Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin 541004 China Shanxi Key Laboratory of Advanced Semiconductor Optoelectronic Devices and Integrated Systems Jincheng Shanxi 048000 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing 400000 China College of Science Beijing Forestry University Beijing 100083 China Jincheng Research Institute of Opto-mechatronics Industry Jincheng Shanxi 048000 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guilin 541004 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Computer Science and Engineering Texas A&M University College Station 77843-3112 TX United States

A 2-bit encoder based on the plasmon-induced transparency (PIT) effect was proposed and investigated in the range from 2 to 7 THz. The Lorentz oscillation coupling model was utilized to confirm the simulation. By tuning the Fermi levels of cross1 and cross2, the 2-bit encoder was achieved. And the minimum MD was 94.73 %, the minimum ER was 12.77 dB, and the maximum IL was 0.32 dB. As a sensor, the structure had a sensitivity of up to 2.33 THz/RIU. In addition, due to the insensitivities to incidence angle and polarization angle, the encoder could be utilized to complex environments. Therefore, these results reveals that our work is significant in terahertz encoders, optical switches, sensors, slow light devices, and modulators. © 2024 The Author(s)

关键词： 2-bit encoder Metamaterial Plasmon-induced transparency (PIT) effect Plasmonic-graphene Terahertz (THz)

A quantitative framework for network resilience evaluation using dynamic bayesian network

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arXiv 2021年

作者： Jiang, Shanqing Yang, Lin Cheng, Guang Gao, Xianming Feng, Tao Zhou, Yuyang School of Cyber Science and Engineering Southeast University Nanjing China National Key Laboratory of Science and Technology on Information System Security Beijing China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China

Measuring and evaluating network resilience has become an important aspect since the network is vulnerable to both uncertain disturbances and malicious attacks. networked systems are often composed of many dynamic components and change over time, which makes it difficult for existing methods to access the changeable situation of network resilience. This paper establishes a novel quantitative framework for evaluating network resilience using the Dynamic Bayesian network. The proposed framework can be used to evaluate the network's multistage resilience processes when suffering various attacks and recoveries. First, we define the dynamic capacities of network components and establish the network's five core resilience capabilities to describe the resilient networking stages including preparation, resistance, adaptation, recovery, and evolution;the five core resilience capabilities consist of rapid response capability, sustained resistance capability, continuous running capability, rapid convergence capability, and dynamic evolution capability. Then, we employ a two-time slices approach based on the Dynamic Bayesian network to quantify five crucial performances of network resilience based on core capabilities proposed above. The proposed approach can ensure the time continuity of resilience evaluation in time-varying networks. Finally, our proposed evaluation framework is applied to different attacks and recovery conditions in typical simulations and real-world network topology. Results and comparisons with extant studies indicate that the proposed method can achieve more accurate and comprehensive evaluation and can be applied to network scenarios under various attack and recovery intensity. Copyright © 2021, The Authors. All rights reserved.

关键词： Recovery

PPA: Preference Profiling Attack Against Federated Learning

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arXiv 2022年

作者： Zhou, Chunyi Gao, Yansong Fu, Anmin Chen, Kai Dai, Zhiyang Zhang, Zhi Xue, Minhui Zhang, Yuqing School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Science China Data61 CSIRO Sydney Australia National Computer Network Intrusion Protection Center University of Chinese Academy of Science China

Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. Copyright © 2022, The Authors. All rights reserved.

关键词： Data privacy