检索结果-内蒙古大学图书馆

20th International Conference on Information security and Cryptology, Inscrypt 2024

作者： Xu, Wenhao Xian, Hequn College of Computer Science and Technology Qingdao University Qingdao266071 China Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences Beijing100085 China Cryptograph and Cyber Security Whampoa Institute Guangzhou510000 China

ISBN: (纸本)9789819647309

In modern relational databases, data security and integrity are crucial, particularly in large-scale, unevenly distributed big data environments. This paper proposes a lossless relational data watermarking scheme based on uneven partitioning, addressing the non-uniformity of data by managing data partitioning within the database. By selecting key attributes and setting appropriate thresholds, data is allocated to partitions of varying sizes and volumes. Different watermark intensities are embedded based on these partitions. Traditional robust watermarking schemes modify carriers to maintain data quality but often lack robustness. To improve this, the proposed scheme uses watermark encoding algorithms to integrate data bits with watermark information, storing the generated auxiliary data. During watermark extraction, the watermark is recovered from this auxiliary data through decoding algorithms. This method enhances watermark capacity by encoding at least one attribute of each data tuple, avoiding original data modifications and common distortion issues. Experimental results show that this scheme, combining uneven partitioning with high-capacity lossless watermarks, offers superior robustness and security compared to traditional methods. It provides an efficient and reliable option for data protection in relational databases, particularly suitable for large-scale and dynamically changing data environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Statistical Analysis of a Deep Learning Based Trimodal Biometric System Using Paired Sampling T-Test

Statistical Analysis of a Deep Learning Based Trimodal Biome...

引用

2023 International Conference on science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Atanda, Oladayo Gbenga Ismaila, Wasiu Afolabi, Adeolu Olabode Awodoye, Olufemi Adeyanju Falohun, Adeleye Samuel Oguntoye, Jonathan Ponmile Landmark University Department of Computer Science Omu-Aran Nigeria Sdg 11 Sustainable Cities and Community Nigeria Lautech Department of Cyber-security Science Ogbomoso Nigeria Lautech Department of Computer Engineering Ogbomoso Nigeria Ajayi Crowther University Department of Computer Engineering Oyo Nigeria

ISBN: (纸本)9798350324785

Biometrics of individuals is distinctive to them and is constant over the course of a person's lifespan. A few trimodal biometric systems involving passive biometrics have adopted Convolutional Neural Network (CNN) technique for image recognition but the technique still suffers problems of overfitting and generalization especially with fewer datasets. Hence, the aim of this paper is to address the problems utilizing improved deep learning strategies namely;CNN-MA and CNN- MMA, and to also carry out their statistical analysis utilizing paired sampling t-test. Five hundred and seventy (570) coloured ear images, and one thousand one hundred and forty (1140) grayscale face and iris images of 190 individuals were captured through the use of a Techno F1 digital camera and CMITECH DMX-10 CCD face-iris camera, respectively. One thousand and twenty-six (1026) samples were used for training while six hundred and eighty-four (684) were used for testing. The system implementation was done on MATLAB 2016a. The statistical t- test analysis between CNN-MA and CNN-MMA showed statistical significance in terms of FPR (p = 0.014), P (p = 0.013) and RA (p = 0.005). The statistical t-test analysis between CNN- MA and CNN also showed statistical significance in terms of FPR (p = 0.000), SEN (p = 0.014), P (p = 0.000) and RA (p = 0.036). CNN-MMA and CNN-MA techniques yielded better FPR, SEN, SP, P, and RA than the CNN. © 2023 IEEE.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

Failed Yet Stored: A First Look at DNS Negative Caching

Failed Yet Stored: A First Look at DNS Negative Caching

引用

2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Zeng, Meng Zhu, Yujia Li, Baiyang Zhang, Yuedong Liu, Qingyun Fang, Binxing Chinese Academy of Sciences Institute of Information Engineering China University of Chinese Academy of Sciences School of Cyber Security China National Computer Network Emergency Response Technical Team/Coordination of China China School of Computer Science and Technology ShenZhen China

ISBN: (纸本)9798350367942

Caching is a critical method for enhancing the efficiency and the security of the Domain Name System (DNS). Initially, only successful domain name resolution results were cached. To mitigate failures in DNS transactions (e.g. NXDomain), the IETF proposed standards, further developed into RFC 9520 as of December 2023. In addition to the basic implementation, RFC 9520 standardizes more sophisticated forms of negative caching. This new standard aims to reduce redundant query retries in DNS traffic and protect resolvers from Denial of Service (DoS) *** this study, we present a comprehensive examination of the specific implementations of Negative Caching in resolvers. We designed and validated a method for measuring negative caching and conducted experiments on 44 public resolvers, including their Do53, DoH, and DoT interfaces. Our findings indicate that while public resolvers generally implement various types of negative caching, some exhibit unexpected cache handling behaviors when encountering specific negative responses. Additionally, we discovered that most public resolvers modify the TTL value of negative responses before returning them to clients. Despite the lack of explicit TTL values for newly specified negative responses, we devised a method to approximate the default TTL values used by public resolvers. © 2024 IEEE.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

Meta Heuristic Image Optimisation in LiDAR Sensor Based on Cloud IoT Network and Deep Learning Algorithms

引用

Remote Sensing in Earth Systems sciences 2025年第2期8卷 566-575页

作者： Meenakshi, K. Manoj, Gunna Diwakar, Meenakshi Prabakaran, Manjula Sravan, Padala Babu, Gaddam Ravindra Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Cyber Security Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad India Department of IT Panimalar Engineering College Chennai India Dept of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh Angallu India Computer Science and Engineering NIIT with learning partner Hainan Normal University Haikou China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

LiDAR technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. The aim of this research is to propose a novel technique in cloud IoT network-based LiDAR sensor imaging with image optimization using a metaheuristic deep learning model. Input has been collected LiDAR image using a cloud IoT module and processed for noise removal and normalization. Then this processed image is classified and optimized utilizing a quantile transfer graph convolutional model with binary firefly whale colony optimization. The experimental analysis is carried out in terms of detection accuracy, random precision, recall, RMSE, and AUC for various LiDAR sensor-based datasets. Comparing the results of the various approaches reveals that the suggested approach outperforms them all with a recall of 94%, a random precision of 95%, and a detection accuracy of 98%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cloud IoT network Convolutional model Image optimization LiDAR sensor Metaheuristic deep learning

来源：评论

学校读者我要写书评

暂无评论

CNN-BO-LSTM: an ensemble framework for prognosis of liver cancer

引用

International Journal of Information Technology (Singapore) 2025年第2期17卷 1103-1109页

作者： K N, Sunil Kumar Kashyap, Pavan P. Bhyratae, Darshan A. Bhyratae, Suhas A. Kalaivani, A. Department of ISE Sri Venkateshwara College of Engineering Bangalore India Department of Electronics & amp Communication Engineering Sri Venkateshwara College of Engineering Bangalore India Computer Science and Engineering Sahyadri College of Engineering and Management Mangaluru India Department of MCA New Horizon College of Engineering Marathahalli Bangalore India Department of CSE-Cyber Security Sri Venkateshwara College of Engineering Bangalore India

computer-assisted diagnosis (CAD) is preferred for cancer identification across the globe, which relies on computerized image processing. The creation of previous CAD instruments involved a semi-automated approach that employed traditional deep learning techniques. Such techniques are not well versed with CAD instruments in terms of accuracy. Therefore, the given manuscript presents a convolutional neural network normalized architecture embedded with Bayesian optimization and long short-term memory (CNN-BO-LSTM) for the identification of liver cancer. Early pre-processing is done on the input magnetic resonance imaging (MRI) scan images to improve clarity. Next, we used dynamic binary classification to apply the accurate and region of interest (ROI) extracting approach. It is followed by automatic retrieval of CNN-based appearances from the ROI approach. For classification purposes, LSTM is used, which categorizes the images as benign or malignant. The proposed design’s testing outcomes, which combine characteristics with CNN-based ROI extraction and LSTM classification, surpassed the current state-of-the-art techniques. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： computer aided diagnosis (CAD) Convolutional neural network (CNN) Features extraction and bayesian optimization Long short term memory (LSTM)

来源：评论

学校读者我要写书评

暂无评论

Application of residual network in household waste classification

Application of residual network in household waste classific...

引用

2023 IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Guo, Yutong Luo, Jie Guo, Weile Chengdu University of Technology College of Computer Science Cyber Security Oxford Brookes College Chengdu China Xi'An University of Architecture and Technology College of Metallurgical Engineering Xi'an China

ISBN: (纸本)9798350399028

With the acceleration of urbanization, the amount of garbage has skyrocketed. This makes the burden of sorting waste by manual means heavier and heavier, resulting in a large amount of waste accumulation. In this context, the classification of household garbage gradually received attention. In order to improve the efficiency of garbage classification, we should deal with the increasingly complicated garbage classification work and make garbage classification intelligent and efficient. In this paper, convolutional neural network intelligent algorithm is used to solve the classification problem of organic and inorganic garbage in daily life. The accuracy can reach 90.34% only with low sequence training. Compared with existing commonly used garbage classification and recognition algorithms, the proposed garbage classification and recognition algorithm has better recognition performance and is more suitable for widespread application. © 2023 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Temporal Analysis and Common Weakness Enumeration (CWE) Code Prediction for Software Vulnerabilities Using Machine Learning

Temporal Analysis and Common Weakness Enumeration (CWE) Code...

引用

International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Kishan Karthik S Suraj Singh S Jeevan Namruth Reddy Minal Moharir Mohana Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India NVIDIA Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Zero-day attacks present a significant challenge in cybersecurity by exploiting vulnerabilities that have not been identified yet. This paper leverages the Common Weakness Enumeration (CWE) system—a widely recognized framework for identifying and categorizing software weaknesses—to predict potential zero-day attacks through temporal analysis. By examining historical CWE data, this study aims to uncover patterns that could indicate emerging vulnerabilities, thereby enabling the implementation of proactive security measures. This research offers a new method of predicting zero-day attacks through a systematic analysis of temporal trends, contributing to developing more complex cybersecurity defences. The work is unique and not previously documented in the literature, highlighting the importance of temporal analysis in forecasting and mitigating cybersecurity threats. The proposed predictive model, which employs statistical methods, time series analysis, and prediction model, achieved an accuracy of 0.66, a precision of 0.65, an F1 score of 0.64, and a recall of 0.66. These results demonstrate the model's balanced performance in effectively identifying relevant CWE codes while minimizing false positives.

关键词： Codes Accuracy Systematics Time series analysis Machine learning Predictive models Software Vectors Surges Information systems

来源：评论

学校读者我要写书评

暂无评论

PpNNT: Multiparty Privacy-Preserving Neural Network Training System

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第1期5卷 370-383页

作者： Feng, Qi He, Debiao Shen, Jian Luo, Min Choo, Kim-Kwang Raymond Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China School of Information Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai201204 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

By leveraging smart devices [e.g., industrial Internet of Things (IIoT)] and real-time data analytics, organizations, such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decision-making. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this article, we propose privacy-preserving deep neural network training (PpNNT), which is designed to support federated learning in the multiparty setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset. © 2020 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Forecasting and managing urban futures: machine learning models and optimization of urban expansion

引用

Asian Journal of Civil engineering 2024年第6期25卷 4673-4682页

作者： Abid, Mohammed Talib Aljarrah, Njood Shraa, Tamer Alghananim, Haneen Marouf Department of Architecture College of Engineering Al-Nahrain University Baghdah Iraq Computer Sciences Faculty of Information Technology and Computer Sciences Ajloun National University Ajloun Jordan Faculty of Science and Information Technology Cyber Security Irbid National University Irbid Jordan Faculty of Engineering Architecture Department Ain Shams University Cairo Egypt

This paper presents a novel method for predicting and controlling urban growth by combining convolutional neural networks (CNN) with Spider Monkey Optimization (SMO) to efficiently use their combined capabilities. This strategy utilizes sophisticated machine learning algorithms to forecast urban development patterns, while optimization technologies enhance these forecasts by including sustainable urban design ideas. Integration plays a crucial role in tackling the difficulties presented by fast urbanization, including issues related to land use, resource administration, and environmental sustainability in the fields of civil and architectural engineering. The study employs large datasets to assess the efficacy of CNN models in detecting urban expansion patterns and enhances these models with optimization scenarios encompassing land use, traffic flow, electricity efficiency, water resources, and waste management, all with the goal of enhancing urban sustainability. The findings demonstrate a significant improvement in the precision of predictions and the ability to maintain positive results, highlighting the crucial role of integrating machine learning and optimization in urban planning methodologies. This method not only provides urban planners and politicians with strong and effective tools for making decisions but also creates opportunities for implementing creative and sustainable solutions for urban growth. The results provide a substantial contribution to the existing urban planning literature by demonstrating the actual use of these technologies in regulating urban expansion. This will stimulate more study in this rapidly evolving subject. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks Machine learning Optimization techniques Spider monkey optimization Sustainable urban planning Urban expansion

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Landscape of Machine Learning and Deep Learning Methodologies in Network security: A Comprehensive Literature Review 2

Unveiling the Landscape of Machine Learning and Deep Learnin...

引用

2nd International Conference on cyber Resilience, ICCR 2024

作者： Saeed, Nouf Majid Sultan Eid Ibrahim, Amer Ali, Liaqat Al-Dmour, Nidal A. Mohammed, Abdul Salam Ghazal, Taher M. American University In The Emirates Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates College Of Engineering Mutah University Department Of Computer Engineering Jordan Skyline University College University City Sharjah Shariah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The dynamic nature of cyber threats offers a continual problem in the field of cybersecurity in the context of the expanding internet environment. This study provides an in-depth assessment of the literature on machine learning (ML) and deep learning (DL) methodologies for network analysis for intrusion detection. This review curates, assesses, and distils method-specific findings while considering temporal or thermal correlations. It provides a recognition of the importance of data in ML and DL approaches, and a comprehensive overview of frequently used network datasets in ML/DL applications, as well as the inherent challenges of adopting ML/DL in the cybersecurity field. The study concludes with well-informed recommendations for future areas of research in this critical domine. © 2024 IEEE.

关键词： Intrusion detection

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：