检索结果-内蒙古大学图书馆

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Qing Yu Zhimin Ding Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China

ISBN: (纸本)9781509037117

In order to solve the problem that FCM algorithm is sensitive to initial clustering center, we use Canopy algorithm to conduct the quick rough clustering. In the meantime, to avoid the blindness of Canopy algorithm, we put forward an improved Canopy-FCM algorithm based on max-min principle. In allusion to the problem that FCM algorithm has high time complexity, this article use the parallel computing frame of MapReduce to design and realize the improved Canopy-FCM algorithm. Experimental result shows: improved Canopy-FCM algorithm based on MapReduce has better clustering quality and running speed than the Canopy-FCM and FCM algorithm based on MapReduce, and the improved Canopy-FCM algorithm based on Hadoop has better speed-up ratio than Canopy-FCM based on Standalone mode.

关键词： Clustering algorithms Algorithm design and analysis Signal processing algorithms Parallel processing Classification algorithms Blindness data collection

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis of Web Text Based on Deep Learning with a Attention Mechanism

Sentiment Analysis of Web Text Based on Deep Learning with a...

引用

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Qing Yu Hui Zhao Kaiwen Jiang Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China

The sentiment analysis of online commentary is a hot research topic in the field of network information mining. Traditional text sentiment analysis methods are mainly based on emotional dictionaries or machine learning. However, it relies on the structure of the sentiment dictionary and the artificial design and extraction features, which limits the generalization ability. In contrast, the deep learning model has a stronger ability to express complex mapping functions from data to emotional semantics. This paper proposes a model combining Bidirectional Gated Recurrent Unit(BGRU) neural network and Convolutional Neural network(CNN), and introduces attention mechanism for text sentiment analysis. The experimental results show that compared with the traditional method, the method effectively improves the accuracy of text sentiment classification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Lightweight Anonymous key Agreement Scheme in V2G networks 2

A Lightweight Anonymous Key Agreement Scheme in V2G Networks

引用

2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

作者： Chendong, Huang Feng, Wang Zhongming, Huang Fujian University of Technology School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (数字)9781665454766

ISBN: (纸本)9781665454766

Electric vehicle access to the grid (V2G) technology enables bidirectional interaction of energy and information between electric vehicles and the grid. The energy transactions between electric vehicles and the power grid is completed by the aggregators. Energy transactions are generally completed through open wireless communication networks, which are vulnerable to network attacks such as eavesdropping, impersonation attack, etc. data transmission in the V2G networks requires to verify the identity of each communication entity to achieve confidential communication. The realization of confidential communication can enhance the security of energy transactions. key agreement is a key technique to solve this problem. Therefore, we propose a lightweight anonymous key agreement scheme for V2G networks. For efficiency, we hire a data center to assist electric vehicles and aggregators in our key agreement scheme. security analysis shows that our scheme can provide anonymity, resist eavesdropping, impersonation and replay attacks, etc. Performance analysis shows that our scheme has lower overall computational cost and communication overhead. © 2022 IEEE.

关键词： network security

来源：评论

学校读者我要写书评

暂无评论

Interesting Near-boundary data: Inferring Model Ownership for DNNs

Interesting Near-boundary Data: Inferring Model Ownership fo...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Zhe Sun Zongwen Yang Zhongyu Huang Yu Zhang Jianzhong Zhang Tianjin Key Laboratory of Network and Data Security Technology College of Cyber Science College of Computer Science Nankai University Tianjin China

Deep neural networks (DNNs) require expensive training, which is why the protection of model intellectual property (IP) is becoming more critical. Recently, model stealing has emerged frequently, and many researchers design model watermarking and fingerprinting for verifying model ownership. However, attacks such as ambiguity statements have been used to break the current defense, which poses a challenge to model ownership verification. Therefore, this paper proposes an interesting near-boundary data as evidence for obtaining model ownership and innovatively proposes to infer model ownership instead of verifying model ownership. In this paper, we propose to generate the initial near-boundary data using an algorithm that adds slight noise to generate adversarial examples. We design a generator to privatize the near-boundary data. Our main observation is that the near-boundary data exhibit results close to the classification boundary in both the source model and its derived stolen model. At the end of this work, we design many experiments to verify the effectiveness of the proposed method. The experimental results demonstrate that model ownership can be inferred with high confidence. Noting that our method does not require the training data to be private, and it is extremely costly for model stealers to reuse our method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Least squares twin SVM decision tree for multi-class classification

Least squares twin SVM decision tree for multi-class classif...

引用

International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

作者： Qing Yu Lihui Wang Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology China

ISBN: (纸本)9781509037117

In order to solve multi-class classification problem in real world, we improved TSVM in this paper. We combined LSTSVM with partial binary tree to improve classification efficiency. Binary tree hierarchy can solved the inseparable regional issues in OVO-SVM and OVA-SVM classification. Experimental results show that it improved the classification accuracy. It also has better speed-up ratio than the OVO-SVM, OVA-SVM. It also reduced the training time. The time advantage is more obvious, especially the data set is large.

关键词： data mining the least squares support vector machine partial binary tree multi-class classification

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Algorithms Design and Implementation Based on Differential Privacy 3rd

Deep Learning Algorithms Design and Implementation Based on ...

引用

3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Xu, Xuefeng Yao, Yanqing Cheng, Lei Key Laboratory of Aerospace Network Security Ministry of industry and information technology School of Cyberspace Science and Technology Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Beijing Key Laboratory of Network Technology Beihang University Beijing China Shenzhen Research Institute of Big Data Shenzhen518000 China

ISBN: (纸本)9783030622220

Deep learning models bear the risks of privacy leakage. Attackers can obtain sensitive information contained in training data with some techniques. However, existing differentially private methods such as Differential Privacy-Stochastic Gradient Descent (DP-SGD) and Differential Privacy-Generative Adversarial network (DP-GAN) are not very efficient as they require to perform sampling multiple times. More importantly, DP-GAN algorithm need public data to set gradient clipping threshold. In this paper, we introduce our refined algorithms to tackle these problems. First, we employ random shuffling instead of random sampling to improve training efficiency. We also test Gaussian and Laplace Mechanisms for clipping gradients and injecting noise. Second, we employ zero Concentrated Differential Privacy (zCDP) to compute overall privacy budget. Finally, we adopt dynamical gradient clipping in DP-GAN algorithm. During each iteration, we random sample training examples and set the average gradients norm as the new threshold. This not only makes the algorithm more robust but also doesn’t increase the overall privacy budget. We experiment with our algorithms on MNIST data sets and demonstrate the accuracies. In our refined DP-SGD algorithm, we achieve test accuracy of 96.58%. In our refined DP-GAN algorithm, we adopt the synthetic data to train models and reach test accuracy of 91.64%. The results show that our approach ensures model usability and provides the capability of privacy protection. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

TMOVF: A Task-Agnostic Model Ownership Verification Framework

TMOVF: A Task-Agnostic Model Ownership Verification Framewor...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Zhe Sun Zhongyu Huang Wangqi Zhao Yu Zhang Jianzhong Zhang College of Cyber Science College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

The protection of model intellectual property is becoming an increasingly important issue. However, the existing methods for protecting model ownership, although effective, have limitations. Firstly, they primarily focus on classification models, and secondly, most of the proposed methods reduce the model's utility. To overcome these shortcomings, this paper proposes a task-agnostic model ownership verification framework based on feature fingerprint, called TMOVF, which separates ownership verification from model task. Our key idea is that model knowledge can be uniquely characterized by the extracted features, which may be high-dimensional, complicated, and difficult to compare for each input sample. Nevertheless, these features contain inherent information that cannot be ignored in cases of piracy. To measure the inheritance of our fingerprint, we introduce outlier detection into model ownership verification, which is a first in the field. By reconstructing the outlier detection algorithm, we extract the feature fingerprints of the victim model and the suspicious model, and compute the outliers of their feature fingerprints. By comparing the results, we can verify the ownership of the models. We conduct extensive experiments to evaluate our framework and demonstrate the inheritability of feature fingerprints in stolen models. Our experiments show that the framework is effective in verifying ownership, regardless of the model task. Additionally, our results demonstrate that our framework is more effective than existing methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FLAP: Federated Learning Aggregation Scheme Based on Privileged Secret Sharing

FLAP: Federated Learning Aggregation Scheme Based on Privile...

引用

2023 International Conference on networking and network Applications, NaNA 2023

作者： Mu, Xianyu Tian, Youliang Xiong, Jinbo Wang, Shuai Gong, Boxiang College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Guizhou Xishan Technology Co. Ltd China

ISBN: (纸本)9798350327380

Federated Learning (FL) is a privacy-preserving machine learning technique that trains models on client devices and only uploads new model gradients to servers for aggregation. However, transmitting the true gradients introduces the risk of reverse inference attack, which can compromise client privacy. To address this issue, we present the federated learning aggregation scheme based on privileged secret sharing (FLAP). Firstly, we present privileged secret sharing by combining the one-time pad with shamir secret sharing. Secondly, we introduce game theory to generate games between servers to resist collusion attacks. Finally, the analysis demonstrates that FLAP enables the server to successfully update the global gradient while ensuring the utmost protection of client privacy and FLAP achiev the effect of resisting collusion attack and reverse inference attack. We have proved through experiments that FLAP is 26.42% more efficient than the current scheme, and has strong robustness to drop out. © 2023 IEEE.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

DEAR: Dual-Level Self-attention GRU for Online Early Prediction of Sepsis 19th

DEAR: Dual-Level Self-attention GRU for Online Early Predic...

引用

19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Zhao, Yu Wu, Yike Liu, Mo Cai, Xiangrui Zhang, Ying Yuan, Xiaojie College of Cyber Science Nankai University Tianjin300350 China College of Computer Science Nankai University Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China College of Journalism and Communication Nankai University Tianjin300350 China Centre for Computational Biology Duke-NUS Medical School 8 College Road Singapore169857 Singapore

ISBN: (纸本)9783031203084

Sepsis is one of the leading causes of death in intensive care units (ICUs). Online early prediction of sepsis has the potential for application to support clinicians. Despite the great success of deep neural networks in modeling electronic health records (EHRs), many architectures are incapable to be applied to online early prediction scenarios due to two major limitations. First, they overlook the earlier signs of the disease which are vital for the early prediction of sepsis. Second, they are unable to provide interpretation of prediction results. To tackle the above limitations, we propose a Dual-level sElf-Attention Gated Recurrent Unit networks model, DEAR. On the one hand, DEAR is able to directly identify and strengthen the important time steps from the precedent memory. Specifically, the cell of DEAR straightforwardly fusion the history of both feature level and temporal level into the current step. On the other hand, DEAR provides interpretability with multi-head attention. Experimental results in the real-world sepsis dataset demonstrate that our model outperforms state-of-the-art methods in terms of both utility and balanced accuracy. In addition, the visualization of multi-head attention weights also indicates that DEAR reveals the importance of different time steps in the early prediction of sepsis onset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intensive care units

来源：评论

学校读者我要写书评

暂无评论

security on a knapsack-type encryption scheme based upon hybrid-model assumption

引用

International Journal of network security 2017年第4期19卷 644-647页

作者： Jin, Zhengping Zhang, Hong Li, Zhongxian State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China National Cybernet Security Limited Tianjin300384 China

Provable security is a reduction that breaking the scheme is usually reduced to solving some basic hard problems, thus the foundation of the scheme's security is the assumption that it is hard to solve the based problems. Due to most existing schemes are founded on single assumption, some encryption schemes, whose security are based on multiple assumptions, have been proposed. Recently, Su and Tsai constructed a knapsack-type encryption scheme based on hybrid-model, and proved it should be more secure than schemes based on single assumption. In this paper, we find that this scheme actually cannot reach the security as claimed. By launching the known message attack, we show Su and Tsai's encryption scheme cannot provide confidentiality, for the adversary could decrypt any ciphertext in this cryptosystem if one of the assumptions does not hold.

关键词： Cryptography

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：