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

4,788 篇 会议
146 册 图书

馆藏范围

4,934 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,239 篇 工学
- 1,743 篇 计算机科学与技术...
- 1,549 篇 信息与通信工程
- 941 篇 电气工程
- 14 篇 软件工程
- 8 篇 电子科学与技术（可...
- 5 篇 仪器科学与技术
- 4 篇 控制科学与工程
- 3 篇 网络空间安全
- 2 篇 动力工程及工程热...
- 2 篇 安全科学与工程
- 1 篇 机械工程
- 1 篇 环境科学与工程（可...
92 篇 管理学
- 90 篇 管理科学与工程(可...
- 4 篇 图书情报与档案管...
- 2 篇 工商管理
9 篇 理学
- 9 篇 数学
- 2 篇 统计学（可授理学、...
- 1 篇 系统科学
3 篇 法学
- 1 篇 法学
- 1 篇 社会学
- 1 篇 公安学

主题

479 篇 bandwidth
396 篇 delay
384 篇 computer science
367 篇 routing
337 篇 throughput
334 篇 quality of servi...
315 篇 protocols
302 篇 telecommunicatio...
278 篇 internet
268 篇 wireless sensor ...
260 篇 traffic control
234 篇 network topology
225 篇 ip networks
222 篇 routing protocol...
205 篇 intelligent netw...
199 篇 computer network...
188 篇 ad hoc networks
181 篇 costs
171 篇 switches
145 篇 performance anal...

机构

19 篇 department of co...
15 篇 chinese acad sci...
14 篇 temple univ dept...
13 篇 univ calif santa...
13 篇 tsinghua univ de...
12 篇 palo alto resear...
11 篇 univ texas dept ...
10 篇 nanjing univ sta...
10 篇 computer enginee...
10 篇 department of el...
10 篇 department of el...
10 篇 department of co...
10 篇 univ massachuset...
9 篇 iowa state univ ...
9 篇 univ notre dame ...
9 篇 penn state univ ...
8 篇 georgia state un...
8 篇 univ chinese aca...
8 篇 department of co...
8 篇 school of comput...

作者

19 篇 wu jie
19 篇 shen haiying
16 篇 j.j. garcia-luna...
14 篇 zaigham mahmood
11 篇 haiying shen
11 篇 garcia-luna-acev...
10 篇 jie wu
10 篇 garcia-luna-acev...
10 篇 dharm singh jat
10 篇 wolf tilman
10 篇 tilman wolf
9 篇 jianping wu
9 篇 j. j. garcia-lun...
9 篇 k. makki
8 篇 m. gerla
8 篇 chunming qiao
8 篇 j. harms
8 篇 yu wei
8 篇 choi baek-young
8 篇 yang yy

语言

4,903 篇 英文
30 篇 中文
1 篇 其他

检索条件"丛书名=Computer Communications and Networks"

共 4934 条记录，以下是81-90 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：

LibGuard: Protecting Sensitive Data In Android Third-Party Libraries From XLDH Attacks 33

LibGuard: Protecting Sensitive Data In Android Third-Party L...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： He, Fannv Wang, Jice Huang, Yuhang Peng, Xiancui Zhang, Yuqing Univ Chinese Acad Sci Natl Comp Network Intrus Protect Ctr Beijing Peoples R China Hainan Univ Sch Cyberspace Secur Haikou Peoples R China

ISBN: (纸本)9798350348439;9798350384611

Mobile app vendors/developers extensively integrate third-party libraries into mobile applications. While they enrich the functions of apps, third-party libraries also bring in security risks. It has been widely studied that malicious third-party libraries could collect users' sensitive data from the host apps and the app backend servers. Recent research has reported a new attack vector - malicious libraries strategically target other vendors' library(SDKs) integrated in the same host app to harvest private user data. In this paper, we found two new dimensions of cross library data harvesting(XLDH) attack with serious privacy impacts that start from two new attack surfaces - accessing sensitive fields and accessing sensitive storage. However, the mitigation scheme, significantly, has not been yet studied. To prevent the leaks of sensitive data due to XLDH activities, we first proposed a mitigation scheme - LibGuard, which has been proven to be effective without affecting user's experience on real-world apps.

关键词： Third-party Library Attack Defense Access control

来源：评论

学校读者我要写书评

暂无评论

DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery networks 33

DeePref: Deep Reinforcement Learning For Video Prefetching I...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Alkassab, Nawras Huang, Chin-Tser Botran, Tania Lorido Univ South Carolina Columbia SC 29208 USA Roblox San Mateo CA USA

ISBN: (纸本)9798350348439;9798350384611

Content Delivery networks carry the majority of Internet traffic, and the increasing demand for video content as a major IP traffic across the Internet highlights the importance of caching and prefetching optimization algorithms. Prefetching aims to make data available in the cache before the requester places its request to reduce access time and improve the Quality of Experience on the user side. Traditional prefetching techniques are well adapted to a particular access pattern, but fail to adapt to sudden variations or randomization in workloads. This paper explores the use of deep reinforcement learning to tackle the changes in users' access patterns and automatically adapt over time to predict future requests. We propose DEEPREF, an auto-aggressive video prefetcher that is sensitive to different storage capacities at edge networks. DEEPREF is agnostic to hardware design, operating systems, and applications in which it utilizes only the video ID to make prefetching decisions online. DEEPREF outperforms baseline approaches that use video content popularity as a building block to statically or dynamically make prefetching decisions. Our results show that DEEPREF, using a real-world dataset, achieves 17% increase in terms of prefetching accuracy and 28% increase in prefetching coverage. We also study the possibility of transfer learning of statistical models from one edge network into another, where unseen user requests from unknown distribution are observed. Our results indicate that DEEPREF effectively adapts to distribution shifts where the increase in prefetching accuracy and prefetching coverage are [30%, 10%], respectively.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Flow Correlator: A Flow Table Cache Management Strategy 33

Flow Correlator: A Flow Table Cache Management Strategy

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： McHale, Luke Gratz, Paul, V Sprintson, Alex Two Six Technol Arlington VA 22203 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9798350348439;9798350384611

Switching, routing, and security functions are the backbone of packet processing networks. Fast and efficient processing of packets requires maintaining the state information for many transient network connections. In particular, modern stateful firewalls, security monitoring devices, and Software-Defined Networking (SDN) dataplanes require maintaining stateful flow tables. These flow tables often grow much larger than can fit on-chip, requiring caching to maintain performance. This paper focuses on improving caching efficiency, an important architectural component of the packet processing data planes. We present a novel predictive approach (Flow Correlator) to network flow table cache management by adapting the Hashed Perceptron binary classifier to improve the reliability and performance of the data plane caching. We also discovered an iterative approach to feature selection and ranking while adapting the Hashed Perceptron mechanism to network flow table cache management. Through extensive experimentation, we demonstrate improved caching efficiency of the proposed Flow Correlator mechanism. We also rigorously validate the performance and generic applicability of our technique across real-world datasets.

关键词： Packet switching

来源：评论

学校读者我要写书评

暂无评论

Explainability-Informed Targeted Malware Misclassification 33

Explainability-Informed Targeted Malware Misclassification

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Card, Quincy Aryal, Kshitiz Gupta, Maanak Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA

ISBN: (纸本)9798350348439;9798350384611

In recent years, there has been a surge in malware attacks across critical infrastructures, requiring further research and development of appropriate response and remediation strategies in malware detection and classification. Several works have used machine learning models for malware classification into categories, and deep neural networks have shown promising results. However, these models have shown its vulnerabilities against intentionally crafted adversarial attacks, which yield misclassification of a malicious file. Our paper explores such adversarial vulnerabilities of neural network based malware classification systems in the dynamic and online analysis environments. To evaluate our approach, we trained Feed Forward Neural networks (FFNN) to classify malware categories based on features obtained from dynamic and online analysis environments. We use the state-of-the-art method, SHapley Additive exPlanations (SHAP), for the feature attribution for malware classification, to inform the adversarial attackers about the features with significant importance in classification decisions. Using the explainability-informed features, we perform targeted misclassification adversarial white-box evasion attacks using the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks against the trained classifier. Our results demonstrated a high evasion rate for some instances of attacks, showing a clear vulnerability of a malware classifier for such attacks. We offer recommendations for a balanced approach and a benchmark for much-needed future research into evasion attacks against malware classifiers, and develop more robust and trustworthy solutions.

关键词： Explainability Adversarial Dynamic Analysis Online Analysis White-box attacks Machine learning Robust Trustworthy

来源：评论

学校读者我要写书评

暂无评论

Enhancing Capacity of Optical Circuit Switching Clos Network in Data Center: Progress and Challenges 33

Enhancing Capacity of Optical Circuit Switching Clos Network...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Okil, Eiji Taka, Haruto Inoue, Takeru Kyoto Univ Kyoto Japan NTT Corp NTT Network Innovat Labs Tokyo Japan

ISBN: (纸本)9798350348439;9798350384611

Future data centers are anticipated to embrace cutting-edge circuit switching technologies, particularly optical switching, renowned for their heightened transmission capacity and energy efficiency. Optical circuit switching guarantees consistent communication quality by establishing dedicated connections for data transmission and maintaining their integrity. Data centers favor Clos-network structures due to their scalability. This paper examines the advancements in designing Clos networks to boost switching capacity while maintaining internal blocking quality. We analyze the characteristics of Clos network design models, providing essentials. We extensively discuss their performances regarding their switching capacities and computation times. Drawing from the review of research progress, we address the challenges in enhancing the switching capacity of Clos networks for future studies.

关键词： Clos network optical circuit switching data center switching capacity blocking probability

来源：评论

学校读者我要写书评

暂无评论

Adversarial Attack Detection for Deep Learning Driving Maneuver Classifiers in Connected Autonomous Vehicles 33

Adversarial Attack Detection for Deep Learning Driving Maneu...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Sen, Tanmoy Shen, Haiying Univ Virginia Dept Comp Sci Charlottesville VA 22904 USA

ISBN: (纸本)9798350348439;9798350384611

Connected and autonomous vehicles (CAVs) will be equipped with onboard deep neural network (DNN) models for processing the data from different sensors and communication units in CAVs and on the roads. In the CAV scenario, each vehicle receives time-series driving signals (e.g., speed, brake status) from nearby vehicles, uses its onboard DNN model for driving maneuver prediction for the nearby vehicles, and then takes corresponding actions for safe and efficient driving. Several black-box adversarial attacks for DNN maneuver classifiers in the CAV scenario have been proposed, in which an attacker deliberately sends false driving signals (i.e., add minimum perturbation to the predicted input) to its nearby vehicle to fool its onboard DNN model to output a wrong class label and cause unwanted traffic incidents or congestion. Though previous research proposed adversarial attack detection methods, the methods are for general DNN models rather than specifically for the time-series driving maneuver classification. To detect such adversarial attacks on the DNN maneuver classifiers in the CAV scenario, in this paper, we analyze the adversarial attacks and propose four different approaches comprised of both statistical and machine learning (ML) based methods. Our trace-driven and real experiments show the combination of all four approaches performs 14% better in accuracy rate and 12% better in precision rate compared to the state-of-the-art statistical and ML-based methods. These improvements are critical for driving safety in CAV scenarios.

关键词： Adversarial attack Connected autonomous vehicle Machine learning

来源：评论

学校读者我要写书评

暂无评论

Fake Base Station Detection and Blacklisting 33

Fake Base Station Detection and Blacklisting

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Purification, Sourav Wuthier, Simeon Kim, Jinoh Kim, Jonghyun Chang, Sang-Yoon Univ Colorado Colorado Springs Colorado Springs CO 80918 USA Texas A&M Univ Commerce Commerce TX 75428 USA Elect & Telecommun Res Inst Daejeon 34129 South Korea

ISBN: (纸本)9798350348439;9798350384611

A fake base station is a well-known security issue in mobile networking. The fake base station exploits the vulnerability in the broadcasting message announcing the base station's presence, which is called SIB1 in telecommunications protocols such as 4G LTE and 5G NR, to get the user equipment to connect to itself. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discover that a fake base station (which engages the user equipment until parts of the connectivity setup and then discontinues with the protocol) can disable the victim user equipment's connectivity for an indefinitely long time, which we validate using our threat prototype against current 4G/5G practice. We design and build a detection and blacklisting identification of the fake base station so that the user equipment can avoid the base station and move on to connecting to a legitimate base station for the connectivity availability. Our detection and blacklisting scheme builds on the standardized 5G protocol and requires the implementation only on the user equipment (no further protocol changes), facilitating practicality. Our scheme uses the real-time information of both the time duration and the number of request transmissions, which features are directly impacted by the fake base station's threat and have not been studied in the previous research. We implement both the base station and the user defense on software-defined radio using open-source 5G software (srsRAN and Open5GS) for validations. We vary the base station implementation to simulate legitimate vs. faulty-but-legitimate vs. fake-and-malicious base stations, where the faulty base station notifies the connectivity disruption and releases the session while the fake base station continues to hold the session. We empirically analyze the detection and identification thresholds, which vary with the fake base station's power and the channel condition. By strategically selecting the thre

关键词： Telecommunications Networking 5G 6G Fake Base Station Detection Blacklisting Security Availability

来源：评论

学校读者我要写书评

暂无评论

Impact of Latency and Bandwidth Limitations on the Safety Performance of Collaborative Perception 33

Impact of Latency and Bandwidth Limitations on the Safety Pe...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Shenkut, Dereje Kumar, B. V. K. Vijaya Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9798350348439;9798350384611

The use of vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communications for the exchange of sensor data on roads is becoming an important strategy to improve the safety of both vehicles and other road users. Termed collaborative perception (CP), this method offers potential solutions to overcome limitations inherent to single-agent vehicle perception systems, including occlusions, non-line-of-sight scenarios, and the poor detection of distant objects. However, the need for exchanging sensor data and extracted information also presents communication challenges, particularly due to latency and bandwidth limitations. Earlier studies have focused on developing machine learning (ML) modules to address latency issues and compress data before transmission. This study investigates the impact of communication latency and bandwidth limitations on collaborative perception. We examine the interrelationships between CP performance and communication limitations. We present the results of numerical experiments using the V2X-Sim dataset on state-of-the-art CP methods. Our findings demonstrate the need for designing a CP system in a connected environment that effectively mitigates the impact of latency and communication interruptions, thus improving the reliability and safety of CP systems.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Intelligent Trajectory-based Approach to UAV Location Integrity Checks 33

Intelligent Trajectory-based Approach to UAV Location Integr...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Shin, Mincheol Chang, Sang-Yoon Kim, Jonghyun Park, Kyungmin Kim, Jinoh Texas A&M Univ Comp Sci Dept Commerce TX 75428 USA Univ Colorado Dept Comp Sci Colorado Springs CO 80918 USA ETRI Cybersecur Res Div Daejeon 34129 South Korea

ISBN: (纸本)9798350348439;9798350384611

While unmanned aerial vehicles (UAVs) are increasingly utilized in many domains, there is a growing concern about location integrity for securely deploying and managing the vehicles. A body of studies tackled this problem, e.g., using hardware sensors, cryptographic mechanisms, and machine learning (ML) approaches, but they concentrate primarily on GPS-related information (e.g., jamming and noise). In this study, we take a different approach that performs the checks by analyzing actual movement information. This new approach keeps track of location updates across the flight path ('trajectory') rather than relying only on point-wise GPS-specific features to test the validity of the location information. To this end, we develop a deep sequence method that takes a sequence of flight data samples with a minimal set of attributes capturing location movement over time. Our extensive experimental results support the feasibility of our approach, showing up to 98.9% accuracy for ensuring location consistency (even without referring to any of the GPS-specific features).

关键词： Unmanned aerial vehicles location integrity location spoofing trajectory sequence-based analysis

来源：评论

学校读者我要写书评

暂无评论

Toward Multimodal Vertical Federated Learning: A Traffic Analysis Case Study 33

Toward Multimodal Vertical Federated Learning: A Traffic Ana...

引用

33rd IEEE International Conference on computer communications and networks (ICCCN)

作者： Sundar, Agnideven Palanisamy Li, Feng Zou, Xukai Gao, Tianchong Indiana Univ Purdue Univ Indianapolis Dept Comp & Informat Technol Indianapolis IN USA Indiana Univ Purdue Univ Indianapolis Dept Comp & Informat Sci Indianapolis IN 46202 USA Southeast Univ Sch Cyber Sci & Engn Nanjing Jiangsu Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing Jiangsu Peoples R China

ISBN: (纸本)9798350348439;9798350384611

Federated Learning (FL) is an emerging subclass of Artificial Intelligence that decentralizes the learning process. Unlike the well-studied Horizontal Federated Learning (HFL), which requires the feature space of all participants to be the same, the newly emerging Vertical Federated Learning (VFL) allows participants to hold different features, provided the sample space is the same. This unique aspect enables VFL to incorporate features from different data modalities, a capability that has not yet been sufficiently explored. Currently, VFL researchers adapt datasets originally used for HFL by splitting the data vertically, whether it is text, tabular, or image data. In this paper, we extend the application of VFL to multimodal datasets, specifically in the field of Intelligent Transportation. We build models by combining local models from participants holding CCTV image datasets and Traffic flow tabular datasets. Due to the absence of suitable existing datasets, we introduce a new dataset, the INDOT traffic dataset, which also supports sequential training across time and distance. Our experiments demonstrate the efficiency of VFL in the multimodal traffic analysis scenario and aim to expand the scope of VFL research.

关键词： Multimodal Vertical Federated Learning Traffic Analysis INDOT Dataset

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494页 << < 5 6 7 8 9 10 11 12 13 14 > >>

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：