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

5,010 篇 会议
280 册 图书
84 篇 期刊文献

馆藏范围

5,374 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,879 篇 工学
- 2,570 篇 计算机科学与技术...
- 1,720 篇 软件工程
- 968 篇 信息与通信工程
- 368 篇 电气工程
- 306 篇 控制科学与工程
- 182 篇 网络空间安全
- 126 篇 动力工程及工程热...
- 125 篇 电子科学与技术（可...
- 119 篇 安全科学与工程
- 111 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
- 43 篇 交通运输工程
614 篇 理学
- 369 篇 数学
- 118 篇 生物学
- 103 篇 系统科学
- 100 篇 统计学（可授理学、...
- 84 篇 物理学
488 篇 管理学
- 337 篇 管理科学与工程(可...
- 189 篇 图书情报与档案管...
- 162 篇 工商管理
242 篇 法学
- 166 篇 法学
- 82 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...
62 篇 经济学
- 62 篇 应用经济学
41 篇 军事学
17 篇 农学
16 篇 教育学
12 篇 文学
1 篇 艺术学

主题

504 篇 security
355 篇 wireless sensor ...
308 篇 computer network...
254 篇 protocols
249 篇 computer archite...
219 篇 intrusion detect...
213 篇 authentication
210 篇 computer communi...
201 篇 ip networks
193 篇 computer crime
172 篇 servers
166 篇 computer securit...
160 篇 network security
158 篇 information secu...
155 篇 internet of thin...
150 篇 distributed comp...
147 篇 computer science
146 篇 cryptography
145 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...
10 篇 department of co...
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...
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

5,022 篇 英文
321 篇 其他
43 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 5374 条记录，以下是1021-1030 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：

Bridging security and Efficiency: Energy-Aware Clustering for Data Encryption in Smart Agriculture

Bridging Security and Efficiency: Energy-Aware Clustering fo...

引用

2025 international conference on Computing, Networking and Communications, ICNC 2025

作者： Khan, Huzaif Purohit, Lokesh Jalooli, Ali California State University Dominguez Hills Department of Computer Science CarsonCA90747 United States

ISBN: (纸本)9798331520960

Securing data communication in smart agriculture is essential to ensure data integrity and confidentiality in distributed sensor networks. While Cheon-Kim-Kim-Song (CKKS) homomorphic encryption offers strong security by allowing computation on encrypted data, its significant computational and energy overhead makes it unsuitable for energy-constrained devices like those in agricultural monitoring systems. We propose a novel framework to enable CKKS encryption in resource-limited settings through an optimized, energy-efficient clustering approach. By reducing the number of direct transmissions through data aggregation at cluster heads, our approach mitigates CKKS's energy demands, making it feasible for low-power sensors. We formulate the clustering problem as an NP-hard optimization problem and present a real-time clustering algorithm that minimizes energy consumption while adapting to dynamic sensor energy levels. To evaluate our approach, we use the Gurobi optimizer to solve the problem to optimality and benchmark our algorithm's performance. Results show that our solution achieves near-optimal energy efficiency, closely approximating the optimal solution while remaining computationally feasible for real-time applications. Additionally, our method improves packet delivery ratios and reduces end-to-end delay. This work provides a pathway for the practical implementation of advanced encryption schemes in energy-constrained environments. © 2025 IEEE.

关键词： NP-hard

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

Federated Learning for Enhanced Intrusion Detection in Smart City Environments

Federated Learning for Enhanced Intrusion Detection in Smart...

引用

international conference on Open Source systems and Technologies (ICOSST)

作者： Soomaiya Hamid Narmeen Zakaria Bawany Center for Computing Research Department of Computer Science and Software Engineering Jinnah University for Women Karachi Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

The integration of IoT devices in smart cities enhances urban infrastructure, services, and governance but also introduces significant cybersecurity challenges. Traditional centralized Intrusion Detection systems (IDS) face several issues, including data privacy concerns and high-power consumption due to centralized data processing. These challenges increase the risks of unauthorized access, data breaches, and privacy violations, undermining user trust and compliance with privacy regulations. Additionally, the centralization of data and processing leads to higher power consumption, making these systems less sustainable for widespread deployment in smart cities. This research addresses these issues by proposing a Federated Learning (FL)based intrusion detection framework for smart cities. FL enables collaborative and privacy-preserving model training across distributed IoT devices, mitigating the need to share sensitive data centrally. By aggregating local model updates, FL ensures data privacy and distributes the computational workload, significantly reducing power consumption compared to traditional centralized systems. The proposed model leverages advanced AI techniques and is trained using the IoTID20 dataset. The Flower framework, utilizing the FedAvg algorithm, facilitates the federated learning process. Our experimental results demonstrate that the global model achieves 98% accuracy, with individual clients achieving accuracies of around 85% to 98%. This approach provides continuous learning mechanisms, anomaly detection, and ensemble learning capabilities, enhancing the resilience of federated intrusion detection systems against emerging threats and adversarial attacks. This research systematically investigates the application of federated learning for intrusion detection in smart city networks, addressing key challenges and advancing the state-of-the-art in decentralized cybersecurity solutions. The proposed framework offers a robust, scalable, and privacyco

关键词： Data privacy Power demand Accuracy Smart cities Federated learning Computational modeling Intrusion detection Data models Internet of Things computer security

来源：评论

学校读者我要写书评

暂无评论

17th international conference on Information systems security, ICISS 2021

17th International Conference on Information Systems Securit...

引用

17th international conference on Information systems security, ICISS 2021

ISBN: (纸本)9783030925703

The proceedings contain 15 papers. The special focus in this conference is on Information systems security. The topics include: Experimental Verification of Estimated Block Size of BKZ Algorithm Against LWE;wiP: Privacy Enabled Biometric Authentication Based on Proof of Decryption Techniques;WiP: QoS Based Recommendation System for Efficient Private Inference of CNN Using FHE;improvement over Montgomery Modular Multiplication;faster Private Rating Update via Integer-Based Homomorphic Encryption;WiP: Slow Rate HTTP Attack Detection with Behavioral Parameters;multi Layer Detection Framework for Spear-Phishing Attacks;vulnerability Analysis and Detection Using Graph Neural networks for Android Operating System;Origin Information Assisted Hybrid Analysis to Detect APT Malware;Raising MIPS Binaries to LLVM IR;automated Identification of Social Media Bots Using Deepfake Text Detection;landcoin: A Practical Protocol for Transfer-of-Asset;wiP: A distributed Approach for Statistical Disclosure Control Technologies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Advanced Machine Learning Approach for Cyber Attack Detection in Cloud Computing

Advanced Machine Learning Approach for Cyber Attack Detectio...

引用

2025 international conference on Intelligent Control, Computing and Communications, IC3 2025

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

ISBN: (纸本)9798331527495

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. © 2025 IEEE.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study on Blockchain: Transforming the World

A Comprehensive Study on Blockchain: Transforming the World

引用

Innovation in Technology (INOCON), IEEE international conference for

作者： Sujata Negi Thakur Sudhanshu Maurya Bhupesh Rawat Department of Computer Science & Engineering Graphic Era Hill University Haldwani Campus Nainital India School of Computing Graphic Era Hill University Bhimtal Campus Nainital India

We are going towards digitization, and while discussing it, “money” is the most common word that comes to everyone’s mind. We have “Bitcoins” to support this. The Blockchain, a shared ledger system, allows for the trading of Bitcoin and other types of digital money. In essence, bitcoins are long sequences of code that have been transformed into currency. Digital money that is created and held electronically is called bitcoin. Bitcoin transfers employ a shared ledger system called a blockchain. It is also being used in a number of other fields, including the government, the healthcare industry, and e-voting systems. These days, people are incredibly concerned about transaction security. Due to its comprehensive security characteristics, blockchain networks are increasingly being used. Throughout the world, people are embracing the blockchain network since it has complete security features. In our work, we have also covered some of the unique features of blockchain besides security. Every invention, including blockchains, must overcome numerous difficulties, as we all very well know. Some of the obstacles that the adoption of blockchain technology faces have been briefly discussed. We have covered the fundamentals of current blockchain technology, as well as its applications, features, and difficulties.

关键词： Technological innovation distributed ledger Government Bitcoin Medical services Regulation Blockchains

来源：评论

学校读者我要写书评

暂无评论

Decentralized implementations of SDN-controllers in SDN-FANETs: a survey

Decentralized implementations of SDN-controllers in SDN-FANE...

引用

4th international conference on distributed Sensing and Intelligent systems (ICDSIS 2023)

作者： A. Eljack M. A. Talib S. Abbas Q. Nasir Department of Computer Science University of Sharjah Sharjah UAE Department of Computer Engineering University of Sharjah Sharjah UAE

ISBN: (数字)9781837240258

The implementation of Software-Defined Networking (SDN) in Flying Ad-hoc networks (FANETs), known as SDN-FANET, has gained significant traction in the past decade. This is particularly due to its eccentric capabilities and features which effectively enhance the operation and performance of Unmanned Aerial Vehicles (UAVs) in various applications. SDN-FANETs introduces adaptability and programmability to the network by centralizing its control. A single entity known as the SDN controller, carries out the network control's management and operation. However, this centralized approach introduces a significant risk: a Single Point of Failure (SPoF) associated with the SDN controller. The failure of the controller can lead to severe network disruptions. Therefore, to tackle this challenge, recent research has focused on decentralizing the SDN controller. This survey presents, compares, and analyzes the latest work done in mitigating the centralization issue. Various approaches to decentralization, such as multi-controller architectures, distributed control, and hybrid models, are discussed based on their effectiveness in improving network scalability, reliability, energy efficiency, routing, security, and cost of implementation. In addition, we categorized these schemes in terms of the main technologies utilized: Cloud & Edge, Blockchain, a hybrid of both, and miscellaneous ones. Our work also highlights important findings from the discussed solutions, and open research issues in the context of SDNFANETs along with some possible future directions. By identifying these research gaps, our survey contributes to the understanding of future research directions in the field of SDN-FANETs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing Real- Time Threat Detection with Feature Engineering Strategies for Intelligent NIDS

Optimizing Real- Time Threat Detection with Feature Engineer...

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Jyoti Verma Monika Sethi Manish Snehi Ishu Sharma Keshav Kaushik Department of Computer Science and Engineering Punjabi University Patiala Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science University of Petroleum and Energy Studies Dehradun India

The escalation of cloud-based network attacks has been observed to be in tandem with the rapid proliferation of cloud-based services worldwide. It has highlighted current network intrusion detection systems' (NIDS) inadequacies in adequately safeguarding against distributed security incidents, particularly within the framework of Internet of Things (loT) networks. The significance of intrusion detection systems in safeguarding computer security cannot be overstated, and considerable research endeavours have been directed toward enhancing NIDS methodologies to counteract network interruptions. Learning-based methods to improve detection accuracy by eliminating correlated, recurring, and irrelevant features are a significant area of emphasis in advancing NIDS. Prior studies have investigated the integration of Machine Learning (ML), Dimensionality Reduction (DR), and Deep Learning (DL) methodologies to enhance the accuracy of classification outcomes on Network Intrusion Detection System (NIDS) datasets. The variability in selected features, design, and kinds of attacks among NIDS datasets requires thoroughly investigating these techniques across diverse datasets. The objective of this article is to furnish an overview of prevailing frameworks through the utilization of benchmark datasets to detect atypical attacks and obtain a comprehensive understanding of the present status of Network Intrusion Detection systems. The efficacy of current Network Intrusion Detection systems is assessed by categorizing feature characteristics and applying diverse machine learning methodologies. The study delves into the topic of feature selection techniques as a means of augmenting anomaly detection. Additionally, they investigate the pre-processing of Internet traffic data. Furthermore, the article explores the potential of conceptualizing virtualized real-time Intelligent NIDS using feature engineering techniques. This paper enhances comprehension of NIDS by consolidating varied rese

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intelligent network traffic analysis: leveraging machine learning for enhanced cybersecurity

Intelligent network traffic analysis: leveraging machine lea...

引用

2nd international conference on Pervasive Computing Advances and Applications (PerCAA 2024)

作者： Seema Kaloria Rakesh Kumar Saxena Deshraj Bairwa Faculty of Computer Science & Engineering Poornima University Jaipur Rajasthan India

ISBN: (数字)9781837243006

Network traffic analysis is one of the necessary processes through which there can be monitoring, securing, and optimizing network operations. It is hard for this approach to notice some unknown or evolving kinds of security threats-mostly those involving zero-day attacks or distributed Denial-of-Service-because cyber threats are developing so fast that nowadays even the most state-of-the-art signature-based methods of traffic analysis are not considered sufficient. This paper presents the design and development of the NTA tool based on ML techniques that will solve the problem where conventional approaches fail to have any integration with unsupervised learning techniques toward the detection of anomalies and classification of network traffic. Utilizing K-means clustering, the tool classifies the network data in an unsupervised manner into normal and malicious traffic clusters. This had been trained on a data set with over 400,000 rows of network traffic data features through IP address and port numbers and timestamps. An extensive pipeline of data preprocessing had been set to clean and normalize the data thus providing quality input for the applied machine learning models. Real-time network anomalies and potential security breaches will be detected by the tool, which empowers network administrators to take necessary steps for improving security protocols. The results show the usability of the developed tool and effectiveness toward the detection of known threats and also unknown ones like DDoS attacks. The application of the K-means clustering algorithm in the constructed system showed efficient grouping of similar network patterns to acquire anomalies in high precision. Also, detection accuracy in this proposed tool is in comparison with traditional systems of NTA and is proved to be more reliable than the proposed methods and relatively low false-positive rates are associated. Scalability tests proved robust performance while dealing with large - scale networks

关键词：

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

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 << < 99 100 101 102 103 104 105 106 107 108 > >>

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：