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7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Yu, Xiaomin Yu, Xiaokun College of Computer and Control Engineering Qiqihar University Qiqihar China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar China Heilongjiang Jiaotong Polytechnic Qiqihar China

ISBN: (纸本)9783031855924

Recently, with the rapid development of communication technology, location-based services (LBS) in indoor environments have penetrated into various aspects of people’s daily lives. Wireless local area networks (WLAN) have become the preferred choice for location-based services due to their broad deployment and low cost. However, the typical non-line-of-sight characteristics of indoor environments result in severe fluctuations in received signal strength (RSS), signal coverage vulnerabilities can exacerbate environmental interference, making positioning results more vulnerable to attacks or tampering. The layout of indoor network elements is a key factor that constrains signal coverage rate. This paper focuses on the optimization of indoor network element layout, proposes an indoor network element layout optimization model based on signal coverage and quality in indoor environments and introduces an Adaptive Simulation Annealing Non-inertial Opposite Particle Swarm Optimization algorithm (ASA-NRPSO). Firstly, a reverse strategy is introduced into the particle flight process of the Particle Swarm Optimization (PSO) algorithm, and high-quality particles are selected for the next generation iteration. In the selection of the reverse strategy, the simulated annealing idea is integrated to adaptively select the reverse strategy based on the needs of the particles at different stages, which avoids the particles falling into local optima. The inertia term in the particle swarm velocity update formula is replaced with group information for non-inertial updates. This can more fully utilize the global information of the population to guide the movement of the next generation of particles and improve the convergence speed of the particle swarm. The elite mutation strategy is used to increase the diversity of particles and improve the global search capability of the algorithm. This method ensures the rapid generation of network element layout to improve the signal coverage rate

关键词： Location based services

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Resilient multi-objective mission planning for UAV formation:A unified framework integrating task pre-and re-assignment

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Defence Technology(防务技术) 2025年第3期45卷 203-226页

作者： Xinwei Wang Xiaohua Gao Lei Wang Xichao Su Junhong Jin Xuanbo Liu Zhilong Deng Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalianLiaoning 116024China School of Mathematics and Big Data Anhui University of Science and TechnologyHuainanAnhui 232001China School of Mathematical Science Dalian University of TechnologyDalianLiaoning 116024China Department of Airborne Vehicle Engineering Naval Aeronautical and Astronautical UniversityYantai 264001China

Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution *** the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be *** improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this *** the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization *** an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient *** in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency *** the dual objective used in the former phase is adapted into a single objective to keep a consistent combat *** cases of different scales demonstrate that the two modules cooperate well with each *** the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is *** the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a *** corresponding animation is accessible at ***/video/BV12t421w7EE for better illustration.

关键词： Cooperative mission planning UAV formation Mission reliability Evolutionary algorithm Contract net protocol

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning 34

NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on ...

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34th ACM Web Conference, WWW 2025

作者： Li, Ken Shi, Bin Wei, Jiazhe Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China School of Distance Education Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9798400712746

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client’s local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom d

关键词： Backdoor Attacks Federated Graph Learning

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Electrochemical dissolution, reduction, and nucleation mechanisms of molybdenum in NaCl-KCl molten salt systems

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Journal of Materials Science and Technology 2025年 238卷 45-54页

作者： Lv, Hongzhan Zhang, Liwen Xi, Xiaoli Nie, Zuoren Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

This study investigates the anodic dissolution and electrochemical behavior of molybdenum in a NaCl-KCl molten salt system at 1023 K. The anodic dissolution process was systematically analyzed, revealing a sequential oxidation pathway of molybdenum into high-valence ions (Mo6+, Mo5+, Mo4+) under varying electrolysis potentials. Electrochemical Impedance Spectroscopy (EIS) demonstrated that the dissolution is governed by both charge transfer and diffusion mechanisms, with reduced impedance at higher potentials facilitating molybdenum dissolution. The reduction behavior of dissolved molybdenum ions was further explored using cyclic voltammetry (CV) and square wave voltammetry (SWV), confirming a multi-step reduction process controlled by diffusion and high reversibility. Nucleation studies using chronoamperometry established that molybdenum deposition follows an instantaneous nucleation mechanism. Morphological analysis of cathodic deposits revealed that current density significantly influences particle size, transitioning from nano-sized spherical particles to larger equiaxed crystals with increasing current density. These findings provide a comprehensive understanding of molybdenum's electrochemical properties in molten salts, offering valuable insights for optimizing electrolysis processes and advancing molybdenum-based material production. © 2025

关键词： Cyclic voltammetry

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Efficient Homomorphic Approximation of Max Pooling for Privacy-Preserving Deep Learning 6th

Efficient Homomorphic Approximation of Max Pooling for Pri...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Zhang, Peng Qiu, Dongyan Duan, Ao Liu, Hongwei The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen518060 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen518118 China

ISBN: (纸本)9789819645657

Privacy-Preserving Deep Learning (PPDL) using Fully Homomorphic Encryption (FHE) addresses potential data privacy exposure risks associated with deploying deep learning models in untrusted cloud environments. FHE-based PPDL enables users to encrypt their data locally, allowing cloud service providers to perform computations directly on the encrypted data without ever accessing it. However, FHE faces challenges in efficiently handling nonlinear computations, which are essential to deep learning frameworks, particularly in implementing activation functions and max pooling layers. To address this challenge, we improve the homomorphic max pooling approximation scheme HMaxPool, and propose an efficient homomorphic max pooling algorithm, denoted as HMaxPool+. Specifically, we first redefine the approximation of the maximum function, and then design sub-algorithm to address the potential output expansion issue. The experimental results validate that both the homomorphic max pooling algorithm itself and the corresponding homomorphic SqueezeNet neural network demonstrate enhancements in computational efficiency and accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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First-principles investigation of adhesion strength and interfacial bonding in Mg/X(X=Ti,Zr,Hf,V,Nd,Cr,Mo,Mn,and Fe)interface

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Journal of Magnesium and Alloys 2025年第1期13卷 83-89页

作者： Longke Bao Zhifu Yao Yuhui Zhang Cuiping Wang Kaihong Zheng Rongpei Shi Xingjun Liu Fusheng Pan State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhen 518055China College of Materials Science and Engineering and Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055China Institute of New Materials Guangdong Academy of SciencesGuangzhou 510650China College of Materials Science and Engineering Xiamen UniversityXiamen 361005China College of Materials Science and Engineering Chongqing UniversityChongqing 400045China

Magnesium alloys have gained extensive applications across various industries,including aerospace,transportation,and civil construction,owing to their excellent combinations of high specific strength and stiffness[1].However,their lim-ited strength due to the lack of effective strengthening phases has hindered their broader industrial applications[2].Never-theless,it has been challenging to achieve significant strength-ening due to the restricted solubility of alloying elements in magnesium[3].Thus,more and more efforts have been made to explore the concept of secondary phase-reinforced magne-sium alloys[2,4,5],where the secondary phase acts as re-inforcing agents within the magnesium matrix,resembling a composite material.

关键词： adhesion strength interfacial bonding aluminum alloys industrial applications solubility strengthening phases alloying elements Mg X interfaces

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Yijun Hao Shusen Yang Fang Li Yifan Zhang Cong Zhao Xuebin Ren Peng Zhao Chenren Xu Shibo Wang National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University Xi’an China National Engineering Laboratory for Big Data Analytics and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Computer Science Peking University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China National Engineering Laboratory for Big Data Analytics XiÃan Jiaotong University XiÃan China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports $3.12\times $ more demand when compared to related existing approaches.

关键词： Topology Network topology Planning IP networks Optical fiber networks Routing Optical fibers Optical feedback Production Graph neural networks

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Wind turbine detection based on high spatial resolution four-band reflectance images 6

Wind turbine detection based on high spatial resolution four...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Deng, Fengying Qiao, Baojun Li, Kaitao Zhao, Limin Chen, Xingfeng Li, Jiaguo Liu, Jun Sun, Yuan Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Aerospace Information Research Institute Chinese Academy of Science Beijing China School of Space Information Space Engineering University Beijing China

ISBN: (纸本)9781510688018

Wind turbine detection is essential for the power industry and regulatory agencies to efficiently and accurately determine their number and spatial distribution. Under complex underlying surface conditions, the detection accuracy of conventional methods is low, especially when dealing with multi-scale wind turbine targets. In addition, the underutilization of the near-infrared (NIR) band limits the capability of optical remote sensing sensors in wind turbine detection. To address the above problems, this paper proposes a wind turbine detection algorithm YOLOv8m-BD based on high spatial resolution four-band reflectance images using YOLOv8m as the baseline model. Firstly, the four-band image is input through channel adaptation modification. Next, the CSPDarknet53 to 2-stage FPN (C2f) module is enhanced by introducing deformable convolution (DCN) to expand the receptive field. Finally, the effectiveness of our design was validated through extensive experiments on a self-built wind turbine dataset. Compared to the YOLOv8m baseline model that only inputs RGB three bands, the improved YOLOv8m-BD achieves an accuracy of 96.4% and a false alarm rate of 2.0%, an increase of 3.4% in accuracy and a decrease of 2.5% in false alarm rate. © 2025 SPIE.

关键词： Proximity sensors

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Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism

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Geophysics 2025年第3期90卷 WA125-WA140页

作者： Liu, Yecheng Li, Diquan Li, Jin Zhang, Xian Central South University Monitoring Ministry of Education Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Changsha China Hunan Provincial Key Laboratory of Non-ferrous Resources and Geological Hazard Detection Changsha China Central South University School of Geoscience and Info-physics Changsha China Hunan Normal University College of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha China Hunan University of Finance and Economics School of Information Technology and Management Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology Changsha China

Strong noise is one of the biggest challenges in controlled-source electromagnetic (CSEM) exploration, which severely affects the quality of the recorded signal. We develop a novel and effective CSEM noise attenuation method that integrates a deep convolutional neural network (DCNN) with a high-quality sounding curve screening mechanism. This method first uses a DCNN to learn the large-scale noise characteristics of CSEM measured data and build a DCNN denoising model for attenuating the noise. Because some impulse noise remains after DCNN denoising, the high-quality sounding curve screening mechanism is innovatively applied to optimize the DCNN denoised data. The core of this method is the smoothness value (SV), which quantitatively evaluates the smoothness of the sounding curve (apparent resistivity curve). In effect, the mechanism segments the overall DCNN denoised data and calculates the corresponding sounding curve and its SV for each segment. The highest-quality sounding curve can be screened out by comparing the SVs of all curves as well as obtaining the highest-quality data segment present in the overall DCNN denoised data. Finally, our method is validated with synthetic and field data, demonstrating its feasibility and effectiveness in attenuating large-scale CSEM noise. The quality of CSEM data, which is heavily affected by noise, is significantly improved after processing by our method. © 2025 Society of Exploration Geophysicists. All rights reserved.

关键词： Deep neural networks

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