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检索条件"丛书名=Computer Communications and Networks"
4934 条 记 录,以下是81-90 订阅
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LibGuard: Protecting Sensitive Data In Android Third-Party Libraries From XLDH Attacks  33
LibGuard: Protecting Sensitive Data In Android Third-Party L...
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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
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 studi... 详细信息
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DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery networks  33
DeePref: Deep Reinforcement Learning For Video Prefetching I...
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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
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... 详细信息
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Flow Correlator: A Flow Table Cache Management Strategy  33
Flow Correlator: A Flow Table Cache Management Strategy
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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
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... 详细信息
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Explainability-Informed Targeted Malware Misclassification  33
Explainability-Informed Targeted Malware Misclassification
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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
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 classi... 详细信息
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Enhancing Capacity of Optical Circuit Switching Clos Network in Data Center: Progress and Challenges  33
Enhancing Capacity of Optical Circuit Switching Clos Network...
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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
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 sw... 详细信息
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Adversarial Attack Detection for Deep Learning Driving Maneuver Classifiers in Connected Autonomous Vehicles  33
Adversarial Attack Detection for Deep Learning Driving Maneu...
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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
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 scena... 详细信息
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Fake Base Station Detection and Blacklisting  33
Fake Base Station Detection and Blacklisting
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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
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... 详细信息
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Impact of Latency and Bandwidth Limitations on the Safety Performance of Collaborative Perception  33
Impact of Latency and Bandwidth Limitations on the Safety Pe...
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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
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... 详细信息
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Intelligent Trajectory-based Approach to UAV Location Integrity Checks  33
Intelligent Trajectory-based Approach to UAV Location Integr...
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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
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 pro... 详细信息
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Toward Multimodal Vertical Federated Learning: A Traffic Analysis Case Study  33
Toward Multimodal Vertical Federated Learning: A Traffic Ana...
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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
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 ... 详细信息
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