检索结果-内蒙古大学图书馆

Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

Digital Communications and Networks 2025年第2期11卷 473-481页

作者： Qi Li Gaozhan Liu Qianqian Zhang Lidong Han Wei Chen Rui Li Jinbo Xiong School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Key Laboratory of Cryptography of Zhejiang Province Hangzhou Normal UniversityHangzhou 311121China Tongda College of Nanjing University of Posts and Telecommunications Yangzhou 225127China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi'an 710071China State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal UniversityXining 810008China Faculty of Software Fujian Normal UniversityFuzhou 350108China

Ciphertext-Policy Attribute-Based Encryption(CP-ABE)enables fine-grained access control on ciphertexts,making it a promising approach for managing data stored in the cloud-enabled Internet of *** existing schemes often suffer from privacy breaches due to explicit attachment of access policies or partial hiding of critical attribute ***,resource-constrained IoT devices,especially those adopting wireless communication,frequently encounter affordability issues regarding decryption *** this paper,we propose an efficient and fine-grained access control scheme with fully hidden policies(named FHAC).FHAC conceals all attributes in the policy and utilizes bloom filters to efficiently locate them.A test phase before decryption is applied to assist authorized users in finding matches between their attributes and the access *** attacks are thwarted by providing unauthorized users with invalid *** heavy computational overhead of both the test phase and most of the decryption phase is outsourced to two cloud ***,users can verify the correctness of multiple outsourced decryption results *** analysis and performance comparisons demonstrate FHAC's effectiveness in protecting policy privacy and achieving efficient decryption.

关键词： Access control Policy hiding Verifiable outsourced computation Cloud IoT

来源：评论

学校读者我要写书评

暂无评论

Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

引用

2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study on Intrusion Detection System for Distributed Blockchain Ledger Security with Using Advanced Smart Contracts 8th

A Comprehensive Study on Intrusion Detection System for Dist...

引用

8th International Conference on information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Vats, Shailender Kumar Peddi, Prasadu Vats, Prashant Department of Computer Application Institute of Computer Science and Engineering Shri Jagdishprasad Jhabarmal Tibrewala University Jhunjhunu Rajasthan Chudela India Uttar Pradesh Noida India Department of Computer Science Engineering and Information Technology Shri Jagdishprasad Jhabarmal Tibrewala University Rajasthan Jhunjhunu India Department of Computer Science and Engineering SCSE Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819766772

The potential of blockchain technology to transform several industries, including supply chains, health care, and banking, has drawn much interest in recent years. Blockchain is becoming widely used, but this has raised questions about its security and ability to withstand attacks and threats. An overview of blockchain security issues and risk-reduction strategies is given in this abstract. The decentralized and unchangeable character of blockchain, which is accomplished through the application of cryptographic algorithms and consensus processes, is its primary security feature. However, it remains susceptible to security flaws such as 51% assaults, smart contract glitches, and compromised private keys. This abstract investigates these flaws and how they might affect the security and integrity of blockchain data. Several strategies have been put out and put into practice to increase blockchain security. The abstract looks at how important it is to set best practices and standards to protect blockchain networks from malicious activity and unauthorized access. Security issues will arise as the blockchain ecosystem develops, requiring constant study and cooperation between government, business, and academia. The appeal for a coordinated effort to address blockchain security issues in their entirety and guarantee the technology’s safe and dependable use across a range of applications and sectors is made in the abstract’s conclusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Specific Emitter Identification based on CNN and Transformer 5

Specific Emitter Identification based on CNN and Transformer

引用

5th International Academic Exchange Conference on Science and technology Innovation, IAECST 2023

作者： Wang, Bin Gao, Ning Wang, Feng The 38th Research Institute of China Electronics Technology Group Corporation Key Laboratory of Aperture Array and Space Application Key Laboratory of Intelligent Information Processing Anhui Hefei230088 China Hefei University of Technology School of Computer and Information Hefei230601 China

ISBN: (纸本)9798350357738

Specific emitter identification(SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. In this paper, we present a novel radiation source identification method named CNNFormer based on a hybrid architecture of Convolutional Neural Network and Transformer, harnessing the strengths of both architectures to enhance feature extraction and sequence modeling. We demonstrate the effectiveness of our approach on simulation datasets, achieving state-of-the-art accuracy in emitter identification. We showcase outstanding performance of our method on simulation datasets, achieving 98.29% three-class identification accuracy the emitter dataset with three devices and 97.60% ten-class identification accuracy on an RF fingerprint dataset with ten XSPR transmitters. Furthermore, our model allows for insight into the key features contributing to identification. This work contributes to enhancing the security and reliability of wireless communication systems. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Security Test Case Prioritization through Ant Colony Optimization Algorithm

引用

computer Systems Science & Engineering 2023年第12期47卷 3165-3195页

作者： Abdulaziz Attaallah Khalil al-Sulbi Areej Alasiry Mehrez Marzougui Mohd Waris Khan Mohd Faizan Alka Agrawal Dhirendra Pandey Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Al-Qunfudah Computer CollegeUmm Al-Qura UniversityMeccaSaudi Arabia College of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Computer Application Integral UniversityLucknowUttar Pradesh226026India Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknowUttar Pradesh226025India

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software *** of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test *** effectively applying test case prioritization,both the time and cost required for developing secure software can be *** paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic *** performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional *** has been applied to a Mobile Payment Wallet application to validate the proposed *** results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD *** ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering *** findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

关键词： Confidentiality integrity authentication non-repudiation resilience authorization Ant Colony Optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Lightweight 6-bit S-boxes with DPA Resistance

引用

IEEE Transactions on Network Science and Engineering 2025年

作者： Yan, Liuyan Li, Lang Song, Qingling Hengyang Normal University College of Computer Science and Technology Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Lightweight S-boxes have a significant impact on cryptographic algorithms for resource-constrained devices in the Internet of Things (IoT). Previous research on such S-oxes has focused on low area and good cryptographic properties, which neglects the resistance to side-channel analysis, especially differential power analysis (DPA). The reVisited transparency order (VTO) is one of the best indicators for evaluating S-boxes against DPA attacks so far. Therefore, this paper presents a scheme for designing 6-bit S-boxes. The designed S-boxes are suitable for lightweight block ciphers and have a certain ability to resist DPA attacks. Specifically, we first identify 23 3-bit S-boxes with the optimal cryptographic properties and the smallest hardware area through systematic screening. Then, the paper investigates the constructions of lightweight 6-bit S-boxes based on traditional algorithm structures. Finally, a novel structure named F-LM-F is proposed for designing lightweight 6-bit S-boxes by combining Feistel structure and Lai-Massey structure. It has been proven through comparison that the S-boxes under F-LM-F structure achieve fewer fixed points and lower VTO than the 6-bit S-box of BipBip cipher, with a 17.61% reduction in hardware area and a 13.58% decrease in CPU cycles © 2013 IEEE.

关键词： Side channel attack

来源：评论

学校读者我要写书评

暂无评论

TB Chest X-Ray Diagnostic Technique Using Deep Learning 3

TB Chest X-Ray Diagnostic Technique Using Deep Learning

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

引用

IEEE Transactions on Computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

Detection of Arrhythmia from ECG Signal Using Bat Algorithm-Based Deep Neural Network 7th

Detection of Arrhythmia from ECG Signal Using Bat Algorithm-...

引用

7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Singh, Kabrambam Rupabanta Dash, Sujata Atta-ur-Rahman Department of Computer Application Maharaja Srirama Chandra Bhanja Deo University Sriramachandra Vihar Odisha Baripada India DRIEMS School of Professional Studies Odisha Tangi Cuttack India College of Computer Science and Information Technology Imam Abdul Rahman Bin Faisal University Damian Saudi Arabia

ISBN: (纸本)9789819950140

Heart disease is one of the deathliest diseases in the world. As the ECG data is widely available, many machine learning and deep learning models have been developed for the automatic Arrhythmia diagnosis system. In this study, BiLSTM with the Bat algorithm has been proposed for the classification of the Normal ECG signal and the Arrhythmia ECG signal. It can detect the abnormality of the heart rhythm. The novelty of this study is the feature selection using the Bat algorithm that enhanced the performance of the classification model. The performance of this method is compared with the existing methods and it gives better performance than the other existing methods. With the proposed method, an accuracy of 99.75% has been achieved using tenfold cross-validation. It can help the risk patient in the early detection of the abnormality of the heart rhythm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Prediction of rainfall-type debris flow in Jiangjiagou based on LSTM-Attention 3

Prediction of rainfall-type debris flow in Jiangjiagou based...

引用

3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Gao, Yuan Li, Yingna Kunming University of Science and Technology College of Faculty of Information Engineering and Automation Kunming China Key Laboratory of Computer Technology Application of Yunnan Province Kunming China

ISBN: (纸本)9781665459112

Through the neural network model, the rainfall-type debris flow in Jiangjiagou is predicted. Based on seven meteorological factors with time series characteristics, such as daily rainfall, previous effective rainfall, ground temperature, and humidity, which have been screened by features, the Jiangjiagou rainfall debris flow prediction and early warning model based on LSTM-Attention is constructed. The results show that: (1) LSTM-Attention model is 0.02 higher than that of the Attention-LSTM model, and the prediction effect is better. (2) The prediction accuracy of LSTM-Attention model for Jiangjiagou rainfall-type debris flow can reach 0.88, which is 0.05, 0.07, 0.04, 0.25, 0.13 higher than that of LSTM model, RNN model, RNN-Attention model, SVM model, and BP model, respectively. Research results: It provides a new idea and method for the prediction and research of rainfall-type debris flow, which can be referenced by scholars. © 2022 IEEE.

关键词： Long short-term memory

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：