2020 29th International Conference on Computer Communications and Networks (ICCCN)最新文献_第8页

Asynchronous Distributed Topology Control for Signature Management in Mobile Networks 面向移动网络签名管理的异步分布式拓扑控制

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209745

Benjamin Campbell

引用次数: 4

Evaluating the Use of QoS for Video Delivery in Vehicular Networks 评价车载网络中视频传输QoS的使用

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209735

P. P. Garrido Abenza, Manuel P. Malumbres, Pablo Piñol Peral, O. López-Granado

{"title":"Evaluating the Use of QoS for Video Delivery in Vehicular Networks","authors":"P. P. Garrido Abenza, Manuel P. Malumbres, Pablo Piñol Peral, O. López-Granado","doi":"10.1109/ICCCN49398.2020.9209735","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209735","url":null,"abstract":"In a near future, video transmission capabilities in intelligent vehicular networks will be essential for deploying high-demanded multimedia services for drivers and passengers. Applications and services like video on demand, iTV, context-aware video commercials, touristic information, driving assistance, multimedia e-call, etc., will be part of the common multimedia service-set of future transportation systems. However, wireless vehicular networks introduce several constraints that may seriously impact on the final quality of the video content delivery process. Factors like the shared-medium communication model, the limited bandwidth, the unconstrained delays, the signal propagation issues, and the node mobility, will be the ones that will degrade video delivery performance, so it will be a hard task to guarantee the minimum quality of service required by video applications. In this work, we will study how these factors impact on the received video quality by using a detailed simulation model of a urban vehicular network scenario. We will apply different techniques to reduce the video quality degradation produced by the transmission impairments like (a) Intra-refresh video coding modes, (b) frame partitioning (tiles/slices), and (c) quality of service at the Medium Access Control (MAC) level. So, we will learn how these techniques are able to fight against the network impairments produced by the hostile environment typically found in vehicular network scenarios. The experiments were carried out with a simulation environment based on the OMNeT++, Veins and SUMO simulators. Results show that the combination of the proposed techniques significantly improves the robustness of video transmission in vehicular networks, paving the way, with a wise collaboration with other techniques, to achieve a robust video delivery system that supports multimedia applications in future intelligent transportation systems.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127396542","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: 4

A Scalable Blockchain-based Approach for Authentication and Access Control in Software Defined Vehicular Networks 软件定义车辆网络中基于可扩展区块链的身份验证和访问控制方法

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209661

Léo Mendiboure, M. Chalouf, F. Krief

{"title":"A Scalable Blockchain-based Approach for Authentication and Access Control in Software Defined Vehicular Networks","authors":"Léo Mendiboure, M. Chalouf, F. Krief","doi":"10.1109/ICCCN49398.2020.9209661","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209661","url":null,"abstract":"Software Defined Vehicular Networking (SDVN) could be the future of the vehicular networks, enabling interoperability between heterogeneous networks and mobility management. Thus, the deployment of large SDVN is considered. However, SDVN is facing major security issues, in particular, authentication and access control issues. Indeed, an unauthorized SDN controller could modify the behavior of switches (packet redirection, packet drops) and an unauthorized switch could disrupt the operation of the network (reconnaissance attack, malicious feedback). Due to the SDVN features (decentralization, mobility) and the SDVN requirements (flexibility, scalability), the Blockchain technology appears to be an efficient way to solve these authentication and access control issues. Therefore, many Blockchain-based approaches have already been proposed. However, two key challenges have not been addressed: authentication and access control for SDN controllers and high scalability for the underlying Blockchain network. That is why in this paper we propose an innovative and scalable architecture, based on a set of interconnected Blockchain sub-networks. Moreover, an efficient access control mechanism and a cross-sub-networks authentication/revocation mechanism are proposed for all SDVN devices (vehicles, roadside equipment, SDN controllers). To demonstrate the benefits of our approach, its performances are compared with existing solutions in terms of throughput, latency, CPU usage and read/write access to the Blockchain ledger. In addition, we determine an optimal number of Blockchain sub-networks according to different parameters such as the number of certificates to store and the number of requests to process.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127353339","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: 12

Data-driven Routing Optimization based on Programmable Data Plane 基于可编程数据平面的数据驱动路由优化

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209716

Qian Li, Jiao Zhang, Tian Pan, Tao Huang, Yun-jie Liu

{"title":"Data-driven Routing Optimization based on Programmable Data Plane","authors":"Qian Li, Jiao Zhang, Tian Pan, Tao Huang, Yun-jie Liu","doi":"10.1109/ICCCN49398.2020.9209716","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209716","url":null,"abstract":"To meet the growing demand for high bandwidth of Multimedia network, IP Network Providers spend millions of dollars overprovisioning bandwidth of their network. However, due to the lack of reasonable traffic scheduling, the over-provisioning network still has a severe issue of utilization imbalance. Traffic Engineering (TE) is proposed to solve this problem. Network measurement and routing optimization strategies are two key components of TE. Effective real-time network measurement provides the basis for the generation of route optimization strategies, which makes the network congestion-aware. Existing out-band network telemetry that transmits extra probes to measure network status has the problem of inaccurate measurement information in the network. Besides, the relationship between complex network status and routing optimization strategy is difficult to describe with an exact mathematical model. Therefore, we propose a novel TE approach, which is called DPRO. It combines In-band Network Telemetry based on programmable language P4 with Reinforcement Learning to minimize network max-link-utilization. Extensive experiments show that our approach significantly outperforms several widely-used baseline methods in terms of max-link-utilization.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131764432","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

Reinforcement Learning Based Congestion Control in a Real Environment 真实环境中基于强化学习的拥塞控制

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209750

Lei Zhang, Kewei Zhu, Junchen Pan, Hang Shi, Yong Jiang, Yong Cui

{"title":"Reinforcement Learning Based Congestion Control in a Real Environment","authors":"Lei Zhang, Kewei Zhu, Junchen Pan, Hang Shi, Yong Jiang, Yong Cui","doi":"10.1109/ICCCN49398.2020.9209750","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209750","url":null,"abstract":"Congestion control plays an important role in the Internet to handle real-world network traffic. It has been dominated by hand-crafted heuristics for decades. Recently, reinforcement learning shows great potentials to automatically learn optimal or near-optimal control policies to enhance the performance of congestion control. However, existing solutions train agents in either simulators or emulators, which cannot fully reflect the real-world environment and degrade the performance of network communication. In order to eliminate the performance degradation caused by training in the simulated environment, we first highlight the necessity and challenges to train a learningbased agent in real-world networks. Then we propose a framework, ARC, for learning congestion control policies in a real environment based on asynchronous execution and demonstrate its effectiveness in accelerating the training. We evaluate our scheme on the real testbed and compare it with state-of-the-art congestion control schemes. Experimental results demonstrate that our schemes can achieve higher throughput and lower latency in comparison with existing schemes.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131404056","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: 7

Predict the Next Attack Location via An Attention-based Fused-SpatialTemporal LSTM 基于注意力的融合时空LSTM预测下一个攻击位置

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209605

Zhuang Liu, Juhua Pu, Nana Zhan, Xingwu Liu

引用次数: 1

Advanced Passive Operating System Fingerprinting Using Machine Learning and Deep Learning 使用机器学习和深度学习的先进被动操作系统指纹识别

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209694

D. Hagos, Martin Løland, A. Yazidi, Ø. Kure, P. Engelstad

{"title":"Advanced Passive Operating System Fingerprinting Using Machine Learning and Deep Learning","authors":"D. Hagos, Martin Løland, A. Yazidi, Ø. Kure, P. Engelstad","doi":"10.1109/ICCCN49398.2020.9209694","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209694","url":null,"abstract":"Securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real-time. An accurate passive Operating System (OS) fingerprinting plays a critical role in effective network management and cybersecurity protection. Passive fingerprinting doesn’t send probes that introduce extra load to the network and hence it has a clear advantage over active fingerprinting since it also reduces the risk of triggering false alarms. This paper proposes and evaluates an advanced classification approach to passive OS fingerprinting by leveraging state-of-the-art classical machine learning and deep learning techniques. Our controlled experiments on benchmark data, emulated and realistic traffic is performed using two approaches. Through an Oracle-based machine learning approach, we found that the underlying TCP variant is an important feature for predicting the remote OS. Based on this observation, we develop a sophisticated tool for OS fingerprinting that first predicts the TCP flavor using passive traffic traces and then uses this prediction as an input feature for another machine learning algorithm for predicting the remote OS from passive measurements. This paper takes the passive fingerprinting problem one step further by introducing the underlying predicted TCP variant as a distinguishing feature. In terms of accuracy, we empirically demonstrate that accurately predicting the TCP variant has the potential to boost the evaluation performance from 84% to 94% on average across all our validation scenarios and across different types of traffic sources. We also demonstrate a practical example of this potential, by increasing the performance to 91.3% on average using a tool for TCP variant prediction in an emulated setting. To the best of our knowledge, this is the first study that explores the potential for using the knowledge of the TCP variant to significantly boost the accuracy of passive OS fingerprinting.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134052057","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

ICCCN 2020 Commentary

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/icccn49398.2020.9209597

引用次数: 0

Precise Identification of Rehabilitation Actions using AI based Strategy 基于AI策略的康复行为精确识别

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209617

Mingjuan Lei, Peng Liu, Qingshan Wang, Qi Wang

{"title":"Precise Identification of Rehabilitation Actions using AI based Strategy","authors":"Mingjuan Lei, Peng Liu, Qingshan Wang, Qi Wang","doi":"10.1109/ICCCN49398.2020.9209617","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209617","url":null,"abstract":"With the development of microelectronics and sensor technologies, there are more and more researchers applying them to human action recognition, most of which are professional motion and rely on specific-designed sensors and wearable de-vices. Meanwhile, the need of rehabilitation training is increasing due to occupational diseases, bad life-style and incorrect exercise habit. However, it is costly and inconvenient to train in clinics and hospitals. To buy or borrow a set of medical training equipment is also unpractical. In this paper, we propose to use smart phones, which have larger computing power and are equipped with richer sensors ever than before, to run artificial intelligence based models and algorithms for identification of rehabilitation actions. Beyond doubt, it will be more convenient to use smart phones instead of professional equipments. Nevertheless, there are still some challenges which prevent it from being put into practice, such as phone deployment, data collection, and model training. We initially conceptualize and implement a smart phone-based accuracy judgment system for rehabilitation action. According to the characteristics of the system, e.g., sensor difference, position variation, and computing power limitation, a supervised and data-sharing learning algorithm is proposed, the operation framework, loss function and regular expression function are carefully selected The experiment on a prototype of the system verifies that the proposed method precisely identifies the rehabilitation actions of testees.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132266000","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: 0

MilliCam: Hand-held Millimeter-Wave Imaging MilliCam:手持毫米波成像

2020 29th International Conference on Computer Communications and Networks (ICCCN) Pub Date : 2020-08-01 DOI: 10.1109/ICCCN49398.2020.9209710

M. Saadat, Sanjib Sur, Srihari Nelakuditi, P. Ramanathan

引用次数: 9