检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

4,982 篇 会议
280 册 图书
89 篇 期刊文献

馆藏范围

5,351 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,876 篇 工学
- 2,567 篇 计算机科学与技术...
- 1,716 篇 软件工程
- 978 篇 信息与通信工程
- 373 篇 电气工程
- 304 篇 控制科学与工程
- 183 篇 网络空间安全
- 125 篇 动力工程及工程热...
- 121 篇 电子科学与技术（可...
- 121 篇 安全科学与工程
- 112 篇 机械工程
- 102 篇 生物工程
- 70 篇 建筑学
- 69 篇 仪器科学与技术
- 54 篇 生物医学工程（可授...
- 45 篇 土木工程
- 44 篇 交通运输工程
615 篇 理学
- 368 篇 数学
- 118 篇 生物学
- 104 篇 系统科学
- 101 篇 统计学（可授理学、...
- 85 篇 物理学
484 篇 管理学
- 334 篇 管理科学与工程(可...
- 185 篇 图书情报与档案管...
- 157 篇 工商管理
240 篇 法学
- 165 篇 法学
- 81 篇 社会学
78 篇 医学
- 65 篇 临床医学
- 63 篇 基础医学(可授医学...
- 51 篇 公共卫生与预防医...
61 篇 经济学
- 61 篇 应用经济学
41 篇 军事学
17 篇 农学
14 篇 教育学
12 篇 文学
1 篇 艺术学

主题

500 篇 security
355 篇 wireless sensor ...
309 篇 computer network...
253 篇 protocols
247 篇 computer archite...
216 篇 intrusion detect...
213 篇 authentication
210 篇 computer communi...
200 篇 ip networks
188 篇 computer crime
171 篇 servers
164 篇 computer securit...
158 篇 information secu...
154 篇 network security
152 篇 distributed comp...
150 篇 internet of thin...
147 篇 computer science
147 篇 cryptography
142 篇 computational mo...
142 篇 internet

机构

25 篇 shandong provinc...
21 篇 key laboratory o...
13 篇 shandong fundame...
12 篇 chitkara univers...
10 篇 information syst...
9 篇 shandong enginee...
9 篇 king saud univer...
9 篇 school of comput...
9 篇 institute of inf...
8 篇 school of comput...
8 篇 department of co...
8 篇 department of co...
8 篇 department of co...
7 篇 key laboratory o...
7 篇 school of comput...
7 篇 department of la...
7 篇 hannam universit...
6 篇 shandong enginee...
6 篇 chinese acad sci...
6 篇 department of el...

作者

17 篇 eyas el-qawasmeh
11 篇 tai-hoon kim
9 篇 hammoudeh mohamm...
9 篇 dominik ślęzak
9 篇 obaidat mohammad...
9 篇 igor kotenko
8 篇 isaac woungang
8 篇 azizah abd manaf
8 篇 mukaddim pathan
7 篇 osvaldo gervasi
7 篇 yanmin zhu
7 篇 mohammad s. obai...
7 篇 barolli leonard
7 篇 lin zhang
7 篇 jasni mohamad za...
7 篇 wei wang
6 篇 klaus wehrle
6 篇 zarai faouzi
6 篇 yunhao liu
6 篇 shahid raza

语言

5,230 篇 英文
90 篇 其他
38 篇 中文
1 篇 葡萄牙文

检索条件"任意字段=International Conference on Security in Computer Networks and Distributed Systems, SNDS 2012"

共 5351 条记录，以下是91-100 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Advanced Machine Learning Approach for Cyber Attack Detection in Cloud Computing

Advanced Machine Learning Approach for Cyber Attack Detectio...

引用

Intelligent Control, Computing and Communications (IC3), international conference on

作者： Shashi Bhushan Singh Yadav Nitin Arvind Shelke Jagendra Singh Yasmeen Deependra Pandey Himanshi Department Computer Science & Engineering BBD Northern India Institute of Technology Lucknow India School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE-DS CVR College of Engineering Hyderabad India Department of ECE Amity School of Engineering and Technology Amity University Lucknow India Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Cloud computing has become the most important part of modern IT infrastructure, which is scalable and flexible. However, with such wide-scale adoption, new vulnerabilities have been introduced into the system, making it a favorite target for cyber-attacks, especially distributed Denial-of-Service (DDoS) and Man-in-the-Cloud (MitC) attacks. security measures in cloud environments are more than ever required to be effective. This study introduces a state-of-the-art approach using machine learning techniques to identify and prevent such attacks; the four machine learning algorithms that have been used here are Support Vector Machine, Artificial Neural Network, Recurrent Neural Network, and k-Nearest Neighbors. A dataset of 3,200 simulated records has been used for the purpose of training and testing; 70% of data were used for training purposes, and 30% of data were used for testing purposes. The models were rated based on precision, running time, and memory usage. The results highlighted that SVM was the strongest model and had an accuracy of 97.86%, while ANN was about 93.4% followed by RNN which was about 89.45% and KNN at 86.7%. This would mean that among the above models, the SVM is the most efficient with respect to real-time attack detection as it is in high precision and has efficiency in resource use. The proposed approach can be very well applied to real-time cloud security systems, offering a robust solution in identifying and mitigating cyber threats in dynamic cloud environments, ensuring enhanced protection against evolving attack vectors.

关键词： Support vector machines Cloud computing Accuracy Recurrent neural networks Computational modeling Machine learning Artificial neural networks Nearest neighbor methods Real-time systems Cyberattack

来源：评论

学校读者我要写书评

暂无评论

Recent Trends in computer networks and distributed systems security 2012

引用

丛书名： Communications in computer and Information Science

2012年

作者： Sabu M. Thampi Albert Y. Zomaya Thorsten Strufe Jose M. Alcaraz-Calero Tony Thomas

ISBN: (数字)9783642341359

ISBN: (纸本)9783642341342

This book constitutes the refereed proceedings of the international conference on Recent Trends in computer networks and distributed systems security, held in Trivandrum, India, in October 2012. The 34 revised full papers and 8 poster presentations were carefully reviewed and selected from 112 submissions. The papers cover various topics in computer networks and distributed systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

distributed Denial of Service Attack Detection by Machine Learning Techniques

Distributed Denial of Service Attack Detection by Machine Le...

引用

Learning and Technology conference (L&T)

作者： Varkha K. Jewani Prafulla E. Ajmire Mohd. Atique Bhisham Sharma Imed Ben Dhaou Suhashini Awadhesh Chaurasia Sant Gadge Baba Amravati University Amravati India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science Hekma School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science RTM Nagpur University Nagpur

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The exponential expansion of computer networks and the internet makes it clear that there is a chance of being attacked and harmed. In the meantime, one of the most crucial defensive measures against the more complex and frequent network attacks is the intrusion detection and prevention systems (IDSs and IPSs). Due to insufficient datasets, anomaly-based research in intrusion detection systems is plagued by imprecise deployment, analysis, and evaluation. The researchers analyzed a variety of datasets, including DARPA98, KDD99, ISC2012, and ADFA13, to assess how well their suggested intrusion detection and intrusion prevention techniques performed. There are several issues: lack of adequate attack coverage, unrepresentative payloads, and anonymised packet information, insufficient traffic volume and diversity, or a deficiency in feature set and metadata. This study is focused on CICIDS-2017 which is the recently updated IDS dataset that meets real-world requirements and is accessible publicly. In addition to benign network flows and distributed denial of service (DDoS) attacks, it contains seven common attack network flows as well. To provide the optimal combination of features for the identification of the attack category, it also assesses the efficacy of several machine learning (ML) algorithms and network traffic feature sets. Even though various ML techniques like Linear Regression, Random Forests, and Decision Tree can be used, and performed very well, Hybrid Algorithm is designed to get better analysis on dataset.

关键词： Accuracy Machine learning algorithms Prevention and mitigation Linear regression Intrusion detection Training data Predictive models Decision trees computer crime Random forests

来源：评论

学校读者我要写书评

暂无评论

DDoS Attacks: Detection Techniques, Challenges, and Modern Practices

DDoS Attacks: Detection Techniques, Challenges, and Modern P...

引用

IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Ramesh Redekar Rajesh Bharati Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Pimpri Pune India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Internet, one of the essential components of today's civilization, serves numerous purposes for individuals, businesses, and society. However, its extensive use has sparked concerns, especially regarding privacy and cybersecurity. Furthermore, cyber dangers are becoming more dangerous, intense, and complicated. distributed Denial of Service (DDoS) attacks have evolved as a prevalent and substantial danger to cybersecurity that may disable the network infrastructures of targeted companies and providers. To guard from DDoS assaults, a many security measures are used, like firewalls and intrusion detection systems. Improving the protective capabilities of IDS frameworks using machine and deep learning, other associated technology, is a popular topic currently. Nevertheless, regardless of considerable improvements, identifying DDoS assaults using machine and deep learning, other associated techs remain a difficulty, particularly when dealing with new DDoS attack. Consequently, this review aims to comprehensively discuss about DDoS attacks by going through the contemporary efforts made in the literature to counter the danger due to the DDoS attacks. First, we investigate certain DDoS attack-related solutions suggested by today’s investigators. Finally, we delve deeper by identifying the domains wherein the DDoS attacks are prone to take place; common challenges in recognizing the DDoS attacks in the IoT circumstance; advantages of Software-Defined Networking (SDN); state-of-the-art practices in the academic community to counter DDoS attack attempts in any networks of IoT or SDN or web-connected devices.

关键词： Deep learning Privacy Reviews Prevention and mitigation Intrusion detection Denial-of-service attack Internet of Things Object recognition computer crime Software defined networking

来源：评论

学校读者我要写书评

暂无评论

20th international conference on Data Science, ICDATA 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

20th International Conference on Data Science, ICDATA 2024, ...

引用

20th international conference on Data Science, ICDATA 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031858550

The proceedings contain 37 papers. The special focus in this conference is on Data Science. The topics include: Trust as a Classification Tool: Analyzing Collaboration in Senate Floor Speeches on Gun Legislation Post-Uvalde and Sandy Hook;an Application of Convolutional Neural networks to Chaotic systems;artificial Intelligence Data Reduction Algorithm for Streaming Readout in High Energy Physics Experiment;user Behavior Based Implicit Personality Detection in Recommendation systems;proactive Management of Delays in the French Railway Network: A Seasonal Machine Learning Based Approach;CNN to BiLSTM: Enhancing Setswana Named Entity Recognition;Assessing the Impact of Homelessness on COVID-19 Hospitalization Rates in Patients with Underlying Medical Conditions Through Explainable AI;leveraging Ensemble Learning Paradigms for B2B E-Commerce Fraud Detection;exploring Equity: Integrating Knowledge Graphs in Fairness Testing Methodologies;explainable Machine Learning Approach for Intelligent Edits of Medicaid Home Healthcare Services Claims;authAttLyzer-V2: Unveiling Code Authorship Attribution Using Enhanced Ensemble Learning Models and Generating Benchmark Dataset;Real-Time AI Voice Clone Detection: A Deep Learning Approach to Safeguard Authenticity;Enhancing security with Automated Boom Gate Access Through License Plate Recognition Utilising YOLOv8 Model;generating Culturally Appropriate Avatars;NLP-Guided Synthesis: Transitioning from Sequential Programs to distributed Programs;Generative AI Techniques for the Simulation of Groundwater Well Data at Hanford Site;Enhancing Financial Analysis with Generative AI: Utilizing Large Language Models for Efficient Data Extraction;Leveraging NLP and Large Language Models for Clinical Documentation Improvement: A Medical AI Chatbot Approach;Harnessing LLMs to Build an Autonomous Marketing Agent;Generative AI Use in One Health, One System as Healthcare is Global;dataDock: An Open Source Data Hub for Research;big Data Techniques i

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Communication Protocols in Blockchain-IoT systems for Optimizing IoT Data Flow Efficiency

Comparative Analysis of Communication Protocols in Blockchai...

引用

Advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Mnar Alnaghes Hong Shen School of Computer Science University of Adelaide Adelaide Australia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The Internet of Things (IoT) is considered an evolving technology, consisting of different types of connected devices that have sensors, specialized hardware, and software. However, considerable challenges related to user data privacy, security, and device integrity are presented by this swift expansion, with digital identity being identified as a significant concern in the online landscape. Established by the World Wide Web Consortium (W3C) [2], Decentralized Identifiers (DIDs) represent a standardized format for defining identity. A peer-to-peer model for managing identity and credentials through self-sovereign identity is facilitated by DIDs, in conjunction with blockchain technology and public-key cryptography. The development of verifiable credentials is enabled by the combination of DIDs and blockchain technology, allowing for the verification of the identities of transacting parties through distributed identity registration and the identification of cryptographic keys necessary for validating electronic authentication during transactions. This integrated framework is regarded as both reliable and trusted. Device registration, verification, and credential revocation are enabled by it. In addition, security is ensured, the privacy of participating nodes is respected, and data integrity is upheld to address several existing challenges, such as data scalability and confidentiality. Trusted management and transaction of IoT data are ensured by the presented IoT data platform. Blockchain, decentralized identifiers, and public key cryptography are integrated by the authors to develop this platform. Self-sovereign identity is granted to each participating device, and dynamic changes in components like consensus algorithms and databases are supported by key features. Different IoT communication protocols are evaluated within a decentralized blockchain infrastructure for identity verification by the authors, focusing on interactions between IoT devices and networked se

关键词： Wireless communication Data privacy Wireless sensor networks Protocols W3C Public key cryptography Blockchains Peer-to-peer computing Internet of Things Optimization

来源：评论

学校读者我要写书评

暂无评论

Classification of Internet of Things Cybersecurity Attacks Using a Hybrid Deep Learning Approach 2nd

Classification of Internet of Things Cybersecurity Attacks U...

引用

2nd international conference on Innovations of Intelligent Informatics, Networking and Cybersecurity, 3INC 2024

作者： Kareem, Eman Karkawi Manaa, Mehdi Ebady Department of Information Networks College of IT University of Babylon Babylon Iraq Intelligent Medical Systems Department College of Sciences Al-Mustaqbal University Babylon51001 Iraq College of Information Technology University of Babylon Babylon Iraq

ISBN: (纸本)9783031810640

IoT devices play an integral part of our digital life today, yet pose significant security risks. These risks allow attackers to carry out various cyberattacks, mainly distributed denial of service (DDoS) attacks. To address these challenges, this paper proposes a hybrid deep learning approach that uses deep neural networks (DNNs) and long short-term memory (LSTM) networks to classify IoT cybersecurity attacks. The binary classification technique of LSTM is applied, whereby the models indicated sufficient results in terms of accuracy and performance, with the LSTM model showing an accuracy of 0.999 and the DNN model showing an accuracy of 0.999. To enhance detection capabilities, we used a comprehensive approach that includes 15 different attack categories. Although the DNN model equals the LSTM model with an accuracy of 0.95 in some scenarios, the hybrid model, which integrates the DNN feature outputs and the LSTM-derived features, showed an overall accuracy of 95.36. We further conducted extensive tests on the Edge-IIoT dataset to validate the effectiveness of our hybrid model. The proposed solution effectively exploits the strengths of the DNN and LSTM architectures, providing a robust framework for detecting and classifying DDoS attacks within IoT networks. These results contribute to a promising approach to mitigate the risks associated with IoT vulnerabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

A Powered-Up Classification of Disabling distributed Network Cloud-Based Attacks Using MLPNN-BP and MLPNN-LM 1

引用

3rd international conference on Machine Intelligence and Smart systems, MISS 2023

作者： Singh, Dhiraj Mishra, Rahul Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (数字)9783031317231

ISBN: (纸本)9783031317224

An Intrusion Detection System (IDS) is a form of network security that monitors for malicious activity. In the cloud, an IDS might operate on individual hosts or over an entire network. DDoS attacks are so named because they involve multiple computers working together to disable a service so that it can be taken offline entirely. By diminishing victim resources, the assaults prevent legitimate users from accessing the targeted services. In a classification problem, the classes are already defined and do not overlap before the method is applied, making classification itself a very straightforward problem to solve. Back Propagation (BP), Multi-Layer Perceptron Neural Network (MLP-NN), Levenberg-Marquardt (LM) method, MLPNN-BP, MLPNN-LM, and Radial Basis Function (RBF) were all proposed in this paper (RBF). It is possible that the BP algorithm can be employed successfully for training Neural networks (NN). After the learning phase, the MLP-NN is able to generalise to completely new data. During the training phase, the weights are optimised to minimise the occurrence of any specified error functions. The weight configuration used as a starting point by the LM algorithm is used by all MLP-NNs in this combination method. After the LM training is complete, MLP-NN will use the validation data's MSE to compute. RBF was developed to address the interpolation issue with multiple variables. This is an example of a local approximation of a NN, and it has a high approximation power, a high classification accuracy, and a fast learning rate. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radial basis function networks

来源：评论

学校读者我要写书评

暂无评论

Scalable Intrusion Detection Using Recurrent Neural networks in distributed Data Engineering systems 3rd

Scalable Intrusion Detection Using Recurrent Neural Networks...

引用

3rd international conference on Advancements in Smart Computing and Information security, ASCIS 2024

作者： Jeyaram, A. Muthukumaravel, A. Chennai India Chennai India

ISBN: (纸本)9783031862892

Traditional Intrusion Detection systems (IDS) frequently rely on anomaly and signature-based methods, which have limits in terms of accuracy, scalability, and flexibility. Anomaly-based systems generate a high number of false positives, causing operational problems, but signature-based solutions cannot protect against zero-day assaults. To overcome these problems, the proposed system improves IDS performance in dispersed data engineering contexts by utilizing Long Short-Term Memory (LSTM) networks, which are a type of Recurrent Neural Network (RNN). Furthermore, network data has temporal correlations, which the LSTM-based IDS can utilize to reduce false positives and improve detection accuracy. The methodology ensures maximum fault tolerance and resiliency while scaling to handle terabytes of real-time data by distributing the LSTM model across a cluster and cloud-based architecture. Research results reveal that the proposed approach outperforms existing IDS approaches. For instance, the KDD Cup 99 dataset, achieved 92.5% accuracy, which is higher than other top algorithms in this scenario defined as: Precision (88.4%), and recall (91.2%). F1 score: 89.7%. However, the scores acquired from the NSL-KDD dataset were 91.2% F1 score and 93.2% accuracy, which is rather low in comparison to our results but can provide us with an elementary understanding of the outliers present in both approaches. Furthermore, the techniques greatly reduced false positives and false negatives by overcoming modern cybersecurity challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

来源：评论

学校读者我要写书评

暂无评论

Recent Trends in computer networks and distributed systems security

Recent Trends in Computer Networks and Distributed Systems S...

引用

2nd international conference on security in computer networks and distributed systems, snds 2014

作者： Pérez, Gregorio Martínez Thampi, Sabu M. Ko, Ryan Shu, Lei University of Murcia Spain Indian Institute of Information Technology and Management - Kerala Trivandrum India The University of Waikato Hamilton New Zealand Guangdong University of Petrochemical Technology Maoming China

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 6 7 8 9 10 11 12 13 14 15 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：