检索结果-内蒙古大学图书馆

Joint Covert and Secure Communication for SWIPT Assisted CNOMA systems

IEEE Internet of Things Journal 2025年第12期12卷 20406-20419页

作者： Huang, Gaojian Lei, Yuxin Li, Xingwang Khan, Wali Ullah Wang, Gongpu Nallanathan, Arumugam Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Luxembourg City1855 Luxembourg Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing100044 China Institute of Internet of Things Wuxi214111 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this paper, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative non-orthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What's more, a warden (Alice) and non-colluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis. © 2014 IEEE.

关键词： Inductive power transmission

来源：评论

学校读者我要写书评

暂无评论

Improving Smart Contract security with Contrastive Learning-Based Vulnerability Detection

Improving Smart Contract Security with Contrastive Learning-...

引用

International Conference on Software Engineering (ICSE)

作者： Yizhou Chen Zeyu Sun Zhihao Gong Dan Hao Key Lab of HCST (PKU) MOE School of Computer Science Peking University Beijing China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an independent entity and feed it into a deep learning model to learn vulnerability patterns by fitting vulnerability labels. It is a pity that they disregard the correlation between contracts, failing to consider the commonalities between contracts of the same type and the differences among contracts of different types. As a result, the performance of these methods falls short of the desired level. To tackle this problem, we propose a novel Contrastive Learning Enhanced Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear. In particular, Clear employs a con-trastive learning (CL) model to capture the fine-grained correlation information among contracts and generates correlation labels based on the relationships between contracts to guide the training pro-cess of the CL model. Finally, it combines the correlation and the semantic information of the contract to detect SCVs. Through an empirical evaluation of a large-scale real-world dataset of over 40K smart contracts and compare 13 state-of-the-art baseline methods. We show that Clear achieves (1) optimal performance over all base-line methods; (2) 9.73%-39.99% higher F1-score than existing deep learning methods.

关键词： Deep learning Training Correlation Smart contracts Semantics Fitting Blockchains

来源：评论

学校读者我要写书评

暂无评论

A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning

arXiv

引用

arXiv 2024年

作者： Xu, Xiaoyang Yang, Mengda Yi, Wenzhe Li, Ziang Wang, Juan Hu, Hongxin Zhuang, Yong Liu, Yaxin Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Department of Computer Science and Engineering University at Buffalo SUNY United States

Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However, these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces a new semi-honest Data Reconstruction Attack on SL, named Feature-Oriented Reconstruction Attack (FORA). In contrast to prior works, FORA relies on limited prior knowledge, specifically that the server utilizes auxiliary samples from the public without knowing any client’s private information. This allows FORA to conduct the attack stealthily and achieve robust performance. The key vulnerability exploited by FORA is the revelation of the model representation preference in the smashed data output by victim client. FORA constructs a substitute client through feature-level transfer learning, aiming to closely mimic the victim client’s representation preference. Leveraging this substitute client, the server trains the attack model to effectively reconstruct private data. Extensive experiments showcase FORA’s superior performance compared to state-of-the-art methods. Furthermore, the paper systematically evaluates the proposed method’s applicability across diverse settings and advanced defense strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIG-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction security Speech processing Overfitting

来源：评论

学校读者我要写书评

暂无评论

Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges

arXiv

引用

arXiv 2023年

作者： Dong, Junhao Chen, Junxi Xie, Xiaohua Lai, Jianhuang Chen, Hao School of Computer Science and Engineering Sun Yat-sen University Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong

Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent years have also witnessed remarkable progress in defense against these tailored adversarial examples in deep medical diagnosis systems. In this exposition, we present a comprehensive survey on recent advances in adversarial attacks and defenses for medical image analysis with a systematic taxonomy in terms of the application scenario. We also provide a unified framework for different types of adversarial attack and defense methods in the context of medical image analysis. For a fair comparison, we establish a new benchmark for adversarially robust medical diagnosis models obtained by adversarial training under various scenarios. To the best of our knowledge, this is the first survey paper that provides a thorough evaluation of adversarially robust medical diagnosis models. By analyzing qualitative and quantitative results, we conclude this survey with a detailed discussion of current challenges for adversarial attack and defense in medical image analysis systems to shed light on future research directions. Code is available on GitHub. Copyright © 2023, The Authors. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Towards Fair Federated Learning via Unbiased Feature Aggregation

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： He, Zeqing Wang, Zhibo Dong, Xiaowei Sun, Peng Ren, Ju Ren, Kui Zhejiang University State Key Laboratory of Blockchain and Data Security China Institute of Blockchain and Data Security Hangzhou310027 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

Federated learning (FL) is a distributed machine learning framework that enables multiple clients to collaboratively train models without raw data exchange. Prior studies on FL mainly focus on optimizing learning performance, enhancing privacy preservation, and improving attack resilience. However, little work studies how to mitigate the unfairness of federated trained models while unfair models would make discriminatory decisions toward certain groups or populations (e.g., favoring males over females), leading to serious ethical concerns. Thus, it is crucial to mitigate model unfairness in FL, yet challenging as this requires centralized access to each data point's fairness-sensitive information (e.g., race, gender), which is prohibited in FL. In this work, we propose a novel fair FL framework FedUFA, where the server can aggregate clients' learned knowledge in an unbiased manner, to obtain fair and high-usability federated trained models. Specifically, to unearth the bias in clients' local data and account for potentially heterogeneous local models, we propose a knowledge distillation-based FL scheme, where clients' knowledge of learned features on a public dataset is amalgamated to the server for aggregation. We train an unbiased feature mapper at the server to remove fairness-sensitive latent features and extract fair representations from clients' submitted raw features. In particular, we design an adversarial training method to train the mapper, which involves a predictor aiming to maximize the prediction accuracy on the FL task and a discriminator intending to help identify fairness-sensitive features. Extensive experiments on real-world datasets demonstrate the effectiveness of FedUFA. © 2025 IEEE. All rights reserved,

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Concrete Structural Crack Damage Classification Using Nonlinear Dimension Reduction and Broad Learning system

Concrete Structural Crack Damage Classification Using Nonlin...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Bingshu Wang Jia Lin Xiaodong Zhuang Guanghui Zhang C.L. Philip Chen School of Software Northwestern Polytechnical University Xi'an China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Concrete structural crack damage classification is of importance for road safety. This paper proposes a new method based on broad neural network for crack damage classification in concrete structures. It includes three stages. Firstly, a pre-trained deep neural network is used to extract the features from crack images. Secondly, principal component analysis is used to project the retrieved features from high dimensions to low dimensions. Thirdly, broad learning system is employed to predict the classification using the low-dimensional features. Experimental results demonstrate that this method reduces the model's training time and improves classification accuracy.

关键词： Training Learning systems Dimensionality reduction Accuracy Artificial neural networks Feature extraction Road safety Cybernetics Principal component analysis Image classification

来源：评论

学校读者我要写书评

暂无评论

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy

arXiv

引用

arXiv 2025年

作者： You, Zhichao Dong, Xuewen Li, Shujun Liu, Ximeng Ma, Siqi Shen, Yulong School of Computer Science & Technology Xidian University China Shaanxi Key Laboratory of Network and System Security Xi’an China University of Kent Canterbury United Kingdom College of Computer and Data Science Fuzhou University Fuzhou China School of System and Computing University of New South Wales CanberraACT2612 Australia

Reconstruction attacks against federated learning (FL) aim to reconstruct users’ samples through users’ uploaded gradients. Local differential privacy (LDP) is regarded as an effective defense against various attacks, including sample reconstruction in FL, where gradients are clipped and perturbed. Existing attacks are ineffective in FL with LDP since clipped and perturbed gradients obliterate most sample information for reconstruction. Besides, existing attacks embed additional sample information into gradients to improve the attack effect and cause gradient expansion, leading to a more severe gradient clipping in FL with LDP. In this paper, we propose a sample reconstruction attack against LDP-based FL with any target models to reconstruct victims’ sensitive samples to illustrate that FL with LDP is not flawless. Considering gradient expansion in reconstruction attacks and noise in LDP, the core of the proposed attack is gradient compression and reconstructed sample denoising. For gradient compression, an inference structure based on sample characteristics is presented to reduce redundant gradients against LDP. For reconstructed sample denoising, we artificially introduce zero gradients to observe noise distribution and scale confidence interval to filter the noise. Theoretical proof guarantees the effectiveness of the proposed attack. Evaluations show that the proposed attack is the only attack that reconstructs victims’ training samples in LDP-based FL and has little impact on the target model’s accuracy. We conclude that LDP-based FL needs further improvements to defend against sample reconstruction attacks effectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

FCAU-Net: A Frequency Channel Attention Convolutional Neural Network for Medical Image Segmentation 9

FCAU-Net: A Frequency Channel Attention Convolutional Neural...

引用

9th IEEE Smart World Congress, SWC 2023

作者： Tao, Chen Chen, Hongyu Wu, Ronghua Zhi, Huixiang Yan, Xiao Liu, Hongzhe Xu, Cheng Jian, Muwei Linyi University School of Information Science and Technology Linyi China The First Affiliated Hospital of Ningbo University Ningbo Clinical Research Center for Hematologic Malignancies Department of Haematology Ningybo China Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing China Shandong University of Finance and Economics School of Computer Science and Technology Jinan China

ISBN: (纸本)9798350319804

Recently, U-Net and its subsequent extensions such as Attention U-Net have emerged as the leading medical image segmentation methods. However, many approaches that combine attention mechanisms with U-Net have overlooked a fundamental issue, that is, they fail to represent channel attention mechanisms using scalars. In this work, we propose a novel method called FCAU-Net, which extends the compression of channel attention mechanism to the frequency domain that combines the multi-spectral attention module with U-Net for medical image segmentation. FCAU-Net has a stronger ability to learn significant features specific to local regions than classic U-Net. In addition, we introduce a new inception block that decomposes the large kernel depth-wise convolution of the inception architecture with two parallel branches of deep convolutions, aiming to further enhance the model's feature representation and semantic information acquisition capabilities while reducing the number of parameters added by the large convolutional kernel. Experiments on multiple medical image segmentation datasets demonstrate that our method achieves better segmentation performance. © 2023 IEEE.

关键词： deep learning Frequency Channel Attention Medical image segmentation

来源：评论

学校读者我要写书评

暂无评论

Minimization of Age of information for Satellite-Terrestrial Covert Communication with a Full-Duplex Receiver

Minimization of Age of Information for Satellite-Terrestrial...

引用

International Conference on Networking and Network Applications (NaNA)

作者： Yizhi Cao Zewei Guo Qifeng Miao Ranran Sun Ji He Xiaochen Li Hangzhou Institute of Technology Xidian University Hangzhou China Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China School of Systems Information Science Future University Hakodate Hakodate Japan School of Computer Science and Technology Xidian University Xi’an China PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

This paper investigates the freshness of information for covert communication in a satellite-terrestrial system consisting of a satellite transmitter Alice, a terrestrial full-duplex receiver Bob, and a terrestrial warden Willie. Particularly, Alice tries to transmit covert information to Bob under the detection of Willie, while Bob emits jamming signals to confuse Willie simultaneously. We first provide theoretical modeling for the average age of information (AOI) of Bob and the covert constraint of Willie. Based on the theoretical model, we then drive a optimal transmit power of Alice to minimize the average AoI with the constraint of covertness requirement. Finally, numerical results are presented to verify the theoretical results and to illustrate the impacts of transmit and jamming power on the covertness and average AoI.

关键词： Satellites Transmitters Rician channels Receivers Full-duplex system information age Space-air-ground integrated networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：