IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)最新文献_第7页

A First Look at the Network Turbulence for Google Stadia Cloud-based Game Streaming 首先看看谷歌体育场云游戏流媒体的网络动荡

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484481

Xiaokun Xu, M. Claypool

引用次数: 7

Data Privacy Security Guaranteed Network Intrusion Detection System Based on Federated Learning 基于联邦学习的数据隐私安全保障网络入侵检测系统

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484545

Jibo Shi, Bin Ge, Yang Liu, Yu Yan, Shuang Li

{"title":"Data Privacy Security Guaranteed Network Intrusion Detection System Based on Federated Learning","authors":"Jibo Shi, Bin Ge, Yang Liu, Yu Yan, Shuang Li","doi":"10.1109/INFOCOMWKSHPS51825.2021.9484545","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484545","url":null,"abstract":"With the development of computer software, the amount of network data has increased geometrically. Therefore, how to quickly identify attacks from a large amount of network information is a meaningful research direction. The intrusion detection system (IDS) is the core contributor to protecting the host from attack. It can distinguish the characteristics of intrusion behavior and the intrusion action from the data of the host. However, with the huge increase in the amount of data now, the efficiency of identifying data characteristics is getting lower and lower. In addition, smart terminal equipment such as notebooks, smart phones and wearable devices are also emerging, and these devices are connected to the internet through wireless or wired means. The physical data generated by terminal equipment involves huge amount of personal sensitive data, which poses a challenge to data privacy and security. Federated learning, as a new type of distributed learning framework, allows training data to be shared among multiple participants without revealing their data privacy. In order to solve the problem of privacy data in intrusion detection,, this paper proposes a network intrusion detection method based on federated learning and conducting experiments on the UNSW-NB15 dataset and CICIDS2018 dataset. The simulation results show that the method proposed in this paper can protect data privacy under the premise of achieving acceptable accuracy of intrusion traffic identification.","PeriodicalId":109588,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132225156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Towards Secure Communication in CR-VANETs Through a Trust-Based Routing Protocol 通过基于信任的路由协议实现cr - vanet中的安全通信

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484515

S. Akter, Mohammad Shahriar Rahman, Md Zakirul Alam Bhuiyan, N. Mansoor

{"title":"Towards Secure Communication in CR-VANETs Through a Trust-Based Routing Protocol","authors":"S. Akter, Mohammad Shahriar Rahman, Md Zakirul Alam Bhuiyan, N. Mansoor","doi":"10.1109/INFOCOMWKSHPS51825.2021.9484515","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484515","url":null,"abstract":"Cognitive Radio Networks (CRNs) promise efficient spectrum utilization by operating over the unused frequencies where Vehicular Ad-hoc Networks (VANETs) facilitate information exchanging among vehicles to avoid accidents, collisions, congestion, etc. Thus, CR enabled vehicular networks (CR-VANETs), a thriving area in wireless communication research, can be the enabler of Intelligent Transportation Systems (ITS) and autonomous driver-less vehicles. Similar to others, efficient and reliable communication in CR-VANETs is vital. Besides, security in such networks may exhibit unique characteristics for overall data transmission performance. For efficient and reliable communication, the proposed routing protocol considers the mobility patterns, spectrum availability, and trustworthiness to be the routing metrics. Hence, the protocol considers the vehicle’s speed, mobility direction, inter-vehicles distance, and node’s reliability to estimate the mobility patterns of a node. Besides, a trust-based reliability factor is also introduced to ensure secure communications by detecting malicious nodes or other external threats. Therefore, the proposed protocol detects malicious nodes by establishing trustworthiness among nodes and preserves security. Simulation is conducted for performance evaluation that shows the proposed routing selects the efficient routing path by discarding malicious nodes from the network and outperforms the existing routing protocols.","PeriodicalId":109588,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128425305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Real-time Packet Loss Detection for TCP and UDP Based on Feature-Sketch 基于特征草图的TCP和UDP实时丢包检测

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484593

Hua Wu, Ya Liu, Guang Cheng, Xiaoyan Hu

{"title":"Real-time Packet Loss Detection for TCP and UDP Based on Feature-Sketch","authors":"Hua Wu, Ya Liu, Guang Cheng, Xiaoyan Hu","doi":"10.1109/INFOCOMWKSHPS51825.2021.9484593","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484593","url":null,"abstract":"Nowadays, networks are often impaired by cyber attacks, which leads to network quality of service degradation. Packet loss is one of the essential and concerning symptoms during these attacks. And thus the real-time detection of packet loss is conducive to network anomaly monitoring. Existing passive packet loss detection methods mainly study the packet loss for TCP using header information and few focus on that of UDP due to its limited header information. Besides, such Deep Packet Inspection (DPI) based packet loss detection is resource consuming and impractical for high-speed network. To address these problems, we propose a novel framework called LossDetection based on packet sampling and Feature-Sketch to detect packet loss in real-time for both TCP and UDP. The Feature-Sketch analyzes ongoing packet flow to extract bidirectional packet-type-based and payload-length-based features using 13 counters for TCP and 8 counters for UDP with constant memory consumption. The feature set was trained on Random Forest (RF) model and eXtreme Gradient Boosting (XGB) model to construct the relationship between these features and the packet loss pattern. The result shows that our methodology can detect packet loss in real-time with an accuracy of 95%-97% even at a sampling rate of 1/256.","PeriodicalId":109588,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117348767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

UAV Placement and Resource Allocation for Multi-hop UAV Assisted Backhaul System 多跳无人机辅助回程系统的无人机布局与资源分配

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484638

Yating Dai, Yijun Guo, Jianjun Hao

引用次数: 3

Evaluating V2V Security on an SDR Testbed 基于SDR测试平台的V2V安全性评估

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484435

Geoff Twardokus, Hanif Rahbari

引用次数: 3

WiFi Dynoscope: Interpretable Real-Time WLAN Optimization WiFi动态镜:可解释的实时WLAN优化

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484543

J. Krolikowski, Ovidiu Iacoboaiea, Zied Ben-Houidi, Dario Rossi

引用次数: 1

Engineering A Large-Scale Traffic Signal Control: A Multi-Agent Reinforcement Learning Approach 大规模交通信号控制工程:多智能体强化学习方法

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484451

Yue Chen, Changle Li, Wenwei Yue, Hehe Zhang, Guoqiang Mao

{"title":"Engineering A Large-Scale Traffic Signal Control: A Multi-Agent Reinforcement Learning Approach","authors":"Yue Chen, Changle Li, Wenwei Yue, Hehe Zhang, Guoqiang Mao","doi":"10.1109/INFOCOMWKSHPS51825.2021.9484451","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484451","url":null,"abstract":"Reinforcement learning is of vital significance in machine learning and is also a promising approach for traffic signal control in urban road networks with assistance of deep neural networks. However, in a large scale urban network, the centralized reinforcement learning approach is beset with difficulties due to the extremely high dimension of joint action space. The multi-agent reinforcement learning (MARL) approach overcomes the high dimension problem by employing distributed local agents whose action space is much smaller. Even though, MARL approach introduces another issue that multiple agents interact with environment simultaneously causing its instability so that training each agent independently may not converge. This paper presents an actor-critic based decentralized MARL approach to control traffic signal which overcomes the shortcomings of both centralized RL approach and independent MARL approach. In particular, a distributed critic network is designed which overcomes the difficulty to train a large-scale neural network in centralized RL approach. Moreover, a difference reward method is proposed to evaluate the contribution of each agent, which accelerates the convergence of algorithm and makes agents optimize policy in a more accurate direction. The proposed MARL approach is compared against the fully independent approach and the centralized learning approach in a grid network. Simulation results demonstrate its effectiveness in terms of average travel speed, travel delay and queue length over other MARL algorithms.","PeriodicalId":109588,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128152579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Trust-based Model for Securing Vehicular Networks Against RSU Attacks 基于信任的车辆网络抵御RSU攻击模型

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484458

Aljawharah Alnasser, Hongjian Sun

{"title":"Trust-based Model for Securing Vehicular Networks Against RSU Attacks","authors":"Aljawharah Alnasser, Hongjian Sun","doi":"10.1109/INFOCOMWKSHPS51825.2021.9484458","DOIUrl":"https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484458","url":null,"abstract":"Intelligent Transportation System (ITS) is one of the Internet of Things (IoT) systems that can achieve reliable transportation by providing communications between vehicles and infrastructure units. The interaction between them is called Vehicle-to-Everything (V2X) communication that is bridged by LTE-V2X protocol. However, a V2X communication link faces a significant challenge in cyber-security. Road entities and Road Side Units (RSUs) are exposed to various cyber-attacks, including internal and external attacks. Internal attackers have valid credentials; thus, detecting them is still a challenge. As a result, a trust model was suggested by existing work to protect the network against internal attackers. In this paper, a global trust-based model is proposed for securing V2X communications against RSU attacks. Trust decision is made based on two levels: distributed and global levels. Each road entity can make its local decision based on the distributed trust model. Additionally, the central server has the responsibility to make the global decision and reduce the impact of various RSU attacks. Also, multiple experiments are conducted with different percentage of malicious RSUs to measure the performance of the proposed model. Simulation results show that the proposed model achieves lowest Packet Dropping Rate (PDR) values for various number of malicious nodes in comparison with existing models.","PeriodicalId":109588,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127465746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

S2: a Small Delta and Small Memory Differencing Algorithm for Reprogramming Resource-constrained IoT Devices S2:用于资源受限物联网设备重编程的小增量和小内存差分算法

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) Pub Date : 2021-05-10 DOI: 10.1109/INFOCOMWKSHPS51825.2021.9484473

Borui Li, Chenghao Tong, Yi Gao, Wei Dong

引用次数: 3