检索结果-内蒙古大学图书馆

5th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020

作者： Zhang, Qionglu Jia, Shijie He, Junlin Zhao, Xinyi Xia, Luning Niu, Yingjiao Jing, Jiwu State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Beijing Certificate Authority Corporation Limited Beijing China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160245

Nowadays sensitive data are commonly stored on personal mobile devices (e.g., laptops and smartphones) or public cloud storage platforms. Data on these platforms are commonly protected with strong cryptography like full disk encryption (FDE), and XTS-AES is one of the most widely used encryption algorithms in FDE. Unfortunately, realizing XTS-AES above the block device layer on flash-based block devices is problematic: it neglects the unique nature of the underlying flash memory, which is the most widely employed storage in the mobile devices and cloud infrastructure. As the tweak values of the physical flash pages mapping to the same logical block address are same, thus the system will suffer from the chosen-plaintext attacks due to the out-of-place update of the flash memory. In this paper, we first introduce the concrete attacks for the prior XTS-AES implementation due to neglecting the unique nature of the underlying flash memory, and then propose SecureXTS, a secure XTS-AES implementation method in flash translation layer (FTL). SecureXTS takes advantage of physical page number and block erasure number to generate the tweak value, without introducing additional metadata and hardware extensions. We provide a proof-of-concept SecureXTS implementation using OpenNFM. The experimental results show that, compared with conventional XTS-AES implementations, our SecureXTS can provide secure data encryption functions and data confidentiality with negligible performance overhead. © 2020 IEEE.

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Vulnerability Time Series Prediction Based on Multivariable LSTM

Vulnerability Time Series Prediction Based on Multivariable ...

引用

IEEE International Workshop on Anti-counterfeiting, security, Identification, ASID

作者： Shuang Wu Congyi Wang Jianping Zeng Chengrong Wu School of Computer Science Fudan University Shanghai China Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education Shanghai China

ISBN: (数字)9781728168791

ISBN: (纸本)9781728168807

Vulnerabilities have been widely exploited to launch various cyberattacks, and become one of the most popular network security problems. Several kinds of research on vulnerabilities have been carried out, such as, mining vulnerability, tracing vulnerability, forecasting vulnerability, and so on. Vulnerability forecasting models make the prediction of zero-day vulnerabilities possible, hence they can help us learn more about the number of vulnerabilities in future days and take defence measures in advance. However, most of these models stem from statistics, which cannot adapt to our application scenario very well. Unlike traditional statistical methods, we propose a vulnerability forecasting method based on multivariable LSTM and carry on experiments on the NVD data set. In comparison with ARIMA, our method perform better in number prediction of vulnerabilities.

关键词： Time series analysis Feature extraction Predictive models Software Mathematical model Forecasting Market research

来源：评论

学校读者我要写书评

暂无评论

MANDERA: Malicious Node Detection in Federated Learning via Ranking

arXiv

引用

arXiv 2021年

作者： Zhu, Wanchuang Zhao, Benjamin Zi Hao Luo, Simon Liu, Tongliang Deng, Ke Data61 CSIRO Sydney Australia Department of Computing Macquarie University Australia. Optus Macquarie Cyber Security Hub Sydney Australia School of Computer Science The University of New South Wales Sydney Australia School of Computer Science The University of Sydney Sydney Australia Center for Statistical Science Department of Industrial Engineering Tsinghua University Beijing China

Byzantine attacks hinder the deployment of federated learning algorithms. Although we know that the benign gradients and Byzantine attacked gradients are distributed differently, to detect the malicious gradients is challenging due to (1) the gradient is high-dimensional and each dimension has its unique distribution and (2) the benign gradients and the attacked gradients are always mixed (two-sample test methods cannot apply directly). To address the above, for the first time, we propose MANDERA which is theoretically guaranteed to efficiently detect all malicious gradients under Byzantine attacks with no prior knowledge or history about the number of attacked nodes. More specifically, we transfer the original updating gradient space into a ranking matrix. By such an operation, the scales of different dimensions of the gradients in the ranking space become identical. The high-dimensional benign gradients and the malicious gradients can be easily separated. The effectiveness of MANDERA is further confirmed by experimentation on four Byzantine attack implementations (Gaussian, Zero Gradient, Sign Flipping, Shifted Mean), comparing with state-of-the-art defenses. The experiments cover both IID and Non-IID datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology 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 Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis

arXiv

引用

arXiv 2022年

作者： Zhao, Xusheng Wu, Jia Peng, Hao Beheshti, Amin Monaghan, Jessica J.M. McAlpine, David Hernandez-Perez, Heivet Dras, Mark Dai, Qiong Li, Yangyang Yu, Philip S. He, Lifang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computing Macquarie University Sydney Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China National Acoustic Laboratories Sydney Australia Department of Linguistics The Australian Hearing Hub Macquarie University Sydney Australia CAEIT Beijing China Department of Computer Science University of Illinois ChicagoIL United States Computer Science & Engineering Lehigh University PA United States

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks. © 2022, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A novel binary modified beluga whale optimization algorithm using ring crossover and probabilistic state mutation for enhanced bladder cancer diagnosis

引用

Informatics in Medicine Unlocked 2024年 50卷

作者： Gharaibeh, Hasan Alawad, Noor Aldeen Nasayreh, Ahmad Al Mamlook, Rabia Emhamed Makhadmeh, Sharif Naser Bashkami, Ayah Abualigah, Laith Ezugwu, Absalom E. Computer Science Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Business Administration Trine University IN United States Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libya Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan Department Medical Laboratory Sciences – Faculty of Science Al Balqa Applied University Salt 19117 Jordan Department of Cyber Security and Cloud Computing Faculty of Information Technology Applied Science Private University Amman 11937 Jordan Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied science research center Applied science private university Amman 11931 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman P.O. Box 346 United Arab Emirates

Bladder cancer (BC) remains a significant global health challenge, requiring the development of accurate predictive models for diagnosis. In this study, a new Binary Modified White Whale Optimization (B-MBWO) algorithm is proposed to address the BC problem. The proposed method utilizes circular transitivity optimization and the Probabilistic State Mutation Algorithm (PSMA) to enhance its optimization performance. The new method is called the BBWORCPS algorithm. High-dimensional and complex medical datasets pose challenges to the original optimization algorithms in addressing the BC problem, motivating the proposed modifications to the original Beluga Whale Optimization algorithm. These enhancements, including quantum-inspired mutation and circular crossing, aim to improve solution space exploration and enhance the algorithm's effectiveness in handling intricate feature spaces. Through comprehensive experiments on BC datasets, the superiority of the BBWORCPS algorithm in terms of feature selection accuracy and computational efficiency is demonstrated compared to existing optimization methods. The obtained findings suggest that BBWORCPS offers a promising approach for developing more precise and reliable predictive models for bladder cancer analysis. © 2024 The Author(s)

关键词： Binary modified beluga whale optimization Bladder cancer Feature selection Machine learning Predictive modeling

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation

arXiv

引用

arXiv 2021年

作者： Peng, Yang Liu, Ping Luo, Yawei Zhou, Pan Xu, Zichuan Liu, Jingen Center for Frontier AI Research Agency for Science Technology and Research Singapore Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Liaoning Dalian China

Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning paradigm, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, which try to mimic the human learning process either by making the student directly copy behavior from the teacher or selecting reliable learning materials, we propose to conduct further exploration to imitate the human learning process from different aspects, i.e., adaptively updating learning materials, selectively imitating teacher behaviors, and analyzing learning materials structures. The explored three components, collaborate together to constitute a new method for unsupervised domain adaptive person re-identification, which is called Human Learning Imitation framework. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Vortex Harmonic Generation by Circularly Polarized Gaussian Beam Interacting with Tilted Target

引用

Physical Review Applied 2021年第1期16卷 014065-014065页

作者： Lingang Zhang Baifei Shen Zhigang Bu Xiaomei Zhang Liangliang Ji Shan Huang M. Xiriai Zhangli Xu Chen Liu Zhizhan Xu State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-intense Laser Science Shanghai Institute of Optics and Fine Mechanics (SIOM) Chinese Academy of Sciences (CAS) Shanghai 201800 China Department of Physics Shanghai Normal University Shanghai 200234 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049 China Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing Jiangsu 210031 China

When a circularly polarized (CP) Gaussian beam normally irradiates a solid plasma target, the spin angular momentum of the CP beam transforms into the orbital angular momentum (OAM) of the high-order harmonics through the spin-orbital interaction; this provides a promising way to obtain intense attosecond pulses carrying OAM. However, normal irradiation faces realistic challenges in experiments, as one cannot extract the harmonic without interfering with the driving laser. Here, we propose a feasible scheme to generate vortex high-order harmonics by using a CP Gaussian beam obliquely incident to the target. Theoretical analyses and simulation results show that the nth-order harmonic is composed of vortex modes with topological charges from l=0 to |l|=n−1. The composition ratio depends on the laser focal size and the incident angle. The obtained number of vortex photons is comparable to the normal incidence case at up to an incident angle of 10∘, which greatly facilitates the experimental arrangement.

关键词： High intensity laser-plasma interactions Optical vortices Polarization of light Spatial profiles of optical beams Spin-orbit coupling Ultrafast optics Relativistic plasmas Particle-in-cell methods

来源：评论

学校读者我要写书评

暂无评论

Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder

Enhancing Intrinsic Adversarial Robustness via Feature Pyram...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Guanlin Li Shuya Ding Jun Luo Chang Liu Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Computer Science Center Shandong Academy of Sciences School of Cyber Security Qilu University of Technology China School of Computer Science and Engineering Nanyang Technological University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Whereas adversarial training is employed as the main defence strategy against specific adversarial samples, it has limited generalization capability and incurs excessive time complexity. In this paper, we propose an attack-agnostic defence framework to enhance the intrinsic robustness of neural networks, without jeopardizing the ability of generalizing clean samples. Our Feature Pyramid Decoder (FPD) framework applies to all block-based convolutional neural networks (CNNs). It implants denoising and image restoration modules into a targeted CNN, and it also constraints the Lipschitz constant of the classification layer. Moreover, we propose a two-phase strategy to train the FPD-enhanced CNN, utilizing ε-neighbourhood noisy images with multi-task and self-supervised learning. Evaluated against a variety of white-box and black-box attacks, we demonstrate that FPD-enhanced CNNs gain sufficient robustness against general adversarial samples on MNIST, SVHN and CALTECH. In addition, if we further conduct adversarial training, the FPD-enhanced CNNs perform better than their non-enhanced versions.

关键词： Noise reduction Robustness Training Image restoration Noise measurement Decoding Neural networks

来源：评论

学校读者我要写书评

暂无评论

A Gaussian Reputation-based Hybrid BFT Consensus with a Formal security Framework

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Yang, Ningbin Tang, Chunming Deng, Zhihong He, Debiao Guangzhou University School of Mathematics and Information Science Guangzhou510006 China Guangzhou University Guangzhou Center for Applied Mathematics Guangzhou510006 China Guangzhou University Key Laboratory of Information Security Technology Jointly by Guangdong Province and Guangzhou City Guangzhou510006 China Wuhan University School of Cyber Science and Engineering Wuhan China Shandong Computer Science Center Jinan250014 China

Blockchain systems have evolved over decades, addressing the inefficiencies and high costs associated with centralized architectures. Among various consensus mechanisms, committee-based hybrid Byzantine Fault Tolerant (BFT) protocols are a fundamental approach to blockchain consensus. However, designing a hybrid BFT consensus protocol that ensures fairness, responsiveness, and formal security remains challenging. In this paper, we propose GRBFT: a Gaussian reputation-based hybrid BFT blockchain consensus protocol with a formal security framework. Our proposed protocol integrates a multilateral Gaussian reputation evaluation to incentivize trusted nodes' participation in the consensus. We use threshold signatures and verifiable random functions (VRFs) to randomly select committee members and leaders, ensuring fair reconfiguration and unbiased sortition. A formal security framework is utilized to design and analyze the blockchain consensus system. Additionally, we design a speculative GRBFT (S-GRBFT) protocol to circumvent the traditional O(n2) leader sortition complexity and reduce the communication to O(n) within a single round. Moreover, we present a secure candidate committee reconfiguration method that efficiently updates members based on their reputation and a Proof-of-Stake (PoS) mechanism. The proposed GRBFT protocol is proven to achieve consistency and liveness under the corruption and liveness parameters. © 2004-2012 IEEE.

关键词： Gaussian distribution

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：