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931-940 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：

Optimizing IoT security:Advanced Intrusion Detection for IoT networks Using Deep Learning Techniques

Optimizing IoT Security:Advanced Intrusion Detection for IoT...

引用

Intelligent Technologies (ACOIT), Asian conference on

作者： Khandakar Rabbi Ahmed Nusrat Ameri Jannat Rosul Nisha Aminul Hoque Towfika Salam Md Istiak Hasan Rial Miyan Research Institute International University of Business Agriculture and Technology Uttara Model Town Dhaka Bangladesh Dept. of Mechatronics Engineering Rajshahi University of Engineering and Technology (RUET) Kazla Rajshahi Bangladesh Physics Discipline Science Engineering and Technology School Khulna University Khulna Bangladesh Dept. of Computer Science and Engineering Jahangirnagar University (JU) Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Bangabandhu Road Tongabari Asian University of Bangladesh Ashulia Dhaka Dept. of Computer Science and Engineering Rajshahi University of Engineering and Technology (RUET) Kazla Rajshahi Bangladesh Dept. of Computer Science and Engineering Aditya College of Engineering (AOCE) Surampelam Kakinada Andhra Pradesh India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The advancement of Internet of Things (IoT) devices has ushered in a new era of connectivity and convenience. Intrusion Detection systems (IDS) are vital for protecting IoT networks by identifying abnormal activities indicative of malicious intent. Network systems face distributed Denial of Service (DDoS), Denial of Service (DoS), and service scans, requiring sophisticated IDS that can quickly detect and neutralize these threats. Current IDS solutions need accuracy, efficiency, flexibility, and scalability improvements to detect attack traffic across various IoT networks effectively. The evolving nature of these attacks necessitates the use of up-to-date datasets. This paper contributes to IoT security by developing and assessing an IDS using the BoTNeTIoT-L01 dataset under Mirai and Gafgyt botnet attacks and different sub-type attacks. We evaluate several machine learning models: Decision Trees (DT), Random Forests (RF), LightGBM, and Multilayer Perceptron (MLP). Our results show that MLP outperforms other models, achieving a precision of $\mathbf{9 6. 4 8 \%}$, recall of $\mathbf{9 6. 2 4 \%}$, $\mathbf{F}$-score of $\mathbf{9 6. 7 2 \%}$, and accuracy of $\mathbf{9 6. 6 5 \%}$. This approach ensures our defence strategies remain effective and responsive to the latest attack vectors.

关键词： Radio frequency Deep learning Technological innovation Accuracy Biological system modeling Scalability Intrusion detection Vectors Internet of Things computer crime

来源：评论

学校读者我要写书评

暂无评论

Autoencoder-based Temporal Convolutional Network for Real-Time Anomaly Detection in Large-Scale Network Traffic

Autoencoder-based Temporal Convolutional Network for Real-Ti...

引用

distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Rana Veer Samara Sihman Bharattej R Raed Hamyd Mamatha Bai B G Sandeep Reddy Sasikumar Gurumoorthy Doctorate of Business Administration National Louis University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Tiruchirappalli India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Nowadays, the rapid growth of network traffic has needed the development of efficient anomaly detection systems to ensure network security and reliability. However, the existing One-Class Support Vector Machine (One-class SVM) model often relay on manual feature engineering which was led to time-consuming and ineffective in detecting unknown threats. Hence, this research proposes an Autoencoder-based Temporal Convolutional Network (AE-TCN) for real-time anomaly detection in large-scale network traffic data. This process begins with the collection of input data from Coburg Intrusion Detection Data Set (CIDDS-001) dataset which consists of real time network traffic data from an internal server and external server. Then, the input data is preprocessed with the help of one-hot encoding to convert the categorical variables into numerical variables. After that, Recursive Feature Elimination (RFE) is employed to select the features namely class label, packets and type of attack by eliminating the irrelevant features from the input data. Finally, AE-TCN is introduced to identify the anomalies in large-scale network traffic data from reconstructed nominal patterns. From the results, the proposed AE-TCN achieved better results in terms of accuracy (99.98%), precision (99.95%), recall (99.26%), and F1 score (99.63%) respectively when compared to existing Convolutional Neural Network (CNN) model.

关键词： Support vector machines Autoencoders Telecommunication traffic Manuals Feature extraction Real-time systems Convolutional neural networks Servers Relays Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

CyberGuardian 2: Integrating LLMs and Agentic AI Assistants for Securing distributed networks 20

CyberGuardian 2: Integrating LLMs and Agentic AI Assistants ...

引用

20th international conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2025

作者： Paduraru, Ciprian Patilea, Catalina Camelia Stefanescu, Alin Department of Computer Science University of Bucharest Romania Institute for Logic and Data Science Romania

ISBN: (纸本)9789897587429

Robust cybersecurity measures are essential to protect complex information systems from a variety of cyber threats, which requires sophisticated security solutions. This paper explores the integration of Large Language Models (LLMs) to improve cybersecurity operations within security Operations Centers (SOCs). The proposed framework has a modular plugin architecture where Agentic AI controls the information flow, includes Retrieval Augmented Generation (RAG), protection methods for human-chatbot interactions and tools for managing tasks such as database interactions, code generation and execution. By utilizing these techniques, the framework aims to streamline the workflows of SOC analysts, allowing them to focus on critical tasks rather than redundant activities. The study also explores the dynamic customization of LLMs based on client data, user experience, potential risks and language preferences to ensure a user-centric approach. The results show improvements in efficiency and effectiveness and highlight the potential of LLMs in cybersecurity applications. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

international conference on Computing, Networking and Communications (ICNC)

作者： Huzaif Khan Lokesh Purohit Ali Jalooli Department of Computer Science California State University Dominguez Hills Carson CA USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

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.

关键词： Smart agriculture Energy consumption Heuristic algorithms Clustering algorithms Data aggregation Approximation algorithms Energy efficiency Real-time systems Reliability Homomorphic encryption

来源：评论

学校读者我要写书评

暂无评论

FERI: Feature Enhancement and Relational Interaction for Image-text Matching

FERI: Feature Enhancement and Relational Interaction for Ima...

引用

international conference on Parallel and distributed systems (ICPADS)

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Shuo Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method.

关键词： computer vision Fuses Image matching Semantics Feature extraction Natural language processing

来源：评论

学校读者我要写书评

暂无评论

Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection

Visually Analyze SHAP Plots to Diagnose Misclassifications i...

引用

IEEE international conference on Data Mining Workshops (ICDM Workshops)

作者： Maraz Mia Mir Mehedi A. Pritom Tariqul Islam Kamrul Hasan Department of Computer Science Tennessee Tech Cookeville TN USA School of Information Studies Syracuse University Syracuse NY USA Department of ECE Tennessee State University Nashville TN USA

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Intrusion detection has been a commonly adopted detective security measures to safeguard systems and networks from various threats. A robust intrusion detection system (IDS) can essentially mitigate threats by providing alerts. In networks based IDS, typically we deal with cyber threats like distributed denial of service (DDoS), spoofing, reconnaissance, brute-force, botnets, and so on. In order to detect these threats various machine learning (ML) and deep learning (DL) models have been proposed. However, one of the key challenges with these predictive approaches is the presence of false positive (FP) and false negative (FN) instances. This FPs and FNs within any black-box intrusion detection system (IDS) make the decision-making task of an analyst further complicated. In this paper, we propose an explainable artificial intelligence (XAI) based visual analysis approach using overlapping SHAP plots that presents the feature explanation to identify potential false positive and false negatives in IDS. Our approach can further provide guidance to security analysts for effective decision-making. We present case study with multiple publicly available network traffic datasets to showcase the efficacy of our approach for identifying false positive and false negative instances. Our use-case scenarios provide clear guidance for analysts on how to use the visual analysis approach for reliable course-of-actions against such threats.

关键词： Visualization Explainable AI Decision making Intrusion detection Telecommunication traffic Reconnaissance Reliability computer crime Bars Software development management

来源：评论

学校读者我要写书评

暂无评论

Lightweight Multi-tier IDS for UAV networks: Enhancing UAV Zero-Day Attack Detection with Honeypot Threat Intelligence 15th

Lightweight Multi-tier IDS for UAV Networks: Enhancing UAV ...

引用

15th EAI international conference on Digital Forensics and Cyber Crime, ICDF2C 2024

作者： Jamil, Abdul Majid Cao, Yue Ahmad, Naveed Sali, Aduwati Binti Alshare, Mohammed Ali Chen, Long Hadi, Hassan Jalil School of Cyber Science and Engineering Wuhan University Wuhan China College of Computer and Information Sciences Riyadh Saudi Arabia Universiti Putra Malaysia Serdang Malaysia Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9783031893629

Unmanned Aerial Vehicles (UAVs) present unique security challenges due to their distributed nature and susceptibility to evolving threats. Current Intrusion Detection systems (IDS) struggle to keep pace with real-time and zero-day attacks, necessitating innovative approaches tailored specifically for UAV environments. This study proposes a Unified Intrusion Detection System (UIDS) designed to bolster UAV security by leveraging a Lightweight Multi-Tier IDS architecture. Our framework integrates multiple detection units deployed across UAV networks, enhancing detection accuracy while minimizing resource consumption. By optimizing the Aho-Corasick algorithm and incorporating Honeypot Intelligence, our system not only identifies known and emerging threats but also facilitates the creation of IDS signatures through collaborative knowledge sharing and honeypot integration. Experimental results demonstrate superior detection rates and reduced false positives, validating the efficacy of our approach in safeguarding UAVs against malicious intrusions. Notably, the Lightweight Multi-Tier IDS achieves significant improvements in detection rates, response times, and proactive threat detection capabilities, boasting an impressive overall detection rate of 99.71% against UAV intrusions. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Zero-day attack

来源：评论

学校读者我要写书评

暂无评论

Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning

Reconnaissance Detection in Decentralised IoT System using M...

引用

international Symposium on Advanced networks and Telecommunication systems (ANTS)

作者： Rajdeep Kumar Dutta Bishal Chhetry Rakesh Matam Ferdous Ahmed Barbhuiya Dept. of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model’s ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats.

关键词： Training Data privacy Federated learning Scalability Reconnaissance Data models Telecommunications Internet of Things Servers Faces

来源：评论

学校读者我要写书评

暂无评论

A Drug Box Recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation

A Drug Box Recognition System Based on Deep Learning and Clo...

引用

international conference on Parallel and distributed systems (ICPADS)

作者： Honglei Zhu Anming Dong Jiguo Yu Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Big Data Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor’s prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it’s hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FL-EVCS: Federated Learning based Anomaly Detection for EV Charging Ecosystem

FL-EVCS: Federated Learning based Anomaly Detection for EV C...

引用

international conference on computer Communications and networks (ICCCN)

作者： Shaurya Purohit Manimaran Govindarasu Department of Electrical and Computer Engineering Iowa State University Ames IA

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

The rapid expansion of electric vehicle (EV) technologies and their seamless integration into smart grids herald a significant transition toward sustainable energy practices, highlighting the need to bolster Electric Vehicle Charging Stations (EVCS). These stations, connected through protocols like ISO 15118 and OCPP, ensure secure interactions between EVs, EVCS, and management systems. However, the rise of Internet of Things (IoT) connectivity exposes EVCS to cyber threats, including distributed Denial of Service (DDoS), Man-in-the-Middle (MitM), and Injection attacks, which threaten the stability of thousands of EVCS and the overall charging infrastructure. In response to these challenges, this paper presents a Federated Learning-based Anomaly Detection System (FL-EVCS), an approach that significantly enhances the cybersecurity framework within EVCS networks. By adopting a federated learning model, FL-EVCS prioritizes data privacy by exchanging model parameters instead of raw data across the network. This method offers a powerful collective defense strategy, considerably enhancing the capabilities of traditional ADS that utilize machine learning algorithms such as KNN, RF, and SVM. In our evaluations, FL-EVCS demonstrated good performance by achieving an accuracy of around 97% and superior F1-scores, compared to traditional ML-based ADS using the CICEVSE2024 dataset. By enhancing EVCS security against cyber threats and supporting EV market growth with a secure user-friendly infrastructure, FL-EVCS markedly increases detection accuracy and efficiency. This approach offers a well-balanced solution for anomaly detection, meeting stringent data privacy requirements and promoting the resilience and expansion of the EVCS ecosystem.

关键词： Data privacy Accuracy Federated learning Computational modeling Ecosystems Data models Electric vehicle charging

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 << < 90 91 92 93 94 95 96 97 98 99 > >>

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：