2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)最新文献_第3页

Cloud Telemetry Modeling via Residual Gauss-Markov Random Fields 基于残差高斯-马尔科夫随机场的云遥测建模

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385544

Nicholas C. Landolfi, Daniel C. O’Neill, S. Lall

引用次数: 1

5G-Serv: Decoupling User Control and Network Control in the 3GPP 5G Network 5G- serv: 3GPP 5G网络中用户控制与网络控制解耦

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385552

Meghna Khaturia, Akshatha Nayak Manjeshwar, P. Jha, A. Karandikar

引用次数: 6

Evaluating the impact of Tower Companies on the telecommunications market 评估铁塔公司对电信市场的影响

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385547

F. Guillemin, V. Q. Rodriguez

引用次数: 2

On-demand Key Distribution for Cloud Networks 云网络的按需密钥分发

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385528

Nicolae Paladi, Marco Tiloca, Pegah Nikbakht Bideh, Martin Hell

引用次数: 1

Toward Operative QoE Models using Virtual Multimedia IP-based Streaming Services 基于虚拟多媒体ip的流媒体服务的操作性QoE模型

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385556

Sofiene Jelassi, G. Rubino

{"title":"Toward Operative QoE Models using Virtual Multimedia IP-based Streaming Services","authors":"Sofiene Jelassi, G. Rubino","doi":"10.1109/ICIN51074.2021.9385556","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385556","url":null,"abstract":"The measurement of the Quality of Experience (QoE) of multimedia streaming services (MMSS) over IP networks may be realized thanks to objective QoE models. They are mathematical functions transforming metrics from technical to user domains. An interesting category of QoE models predicts QoE scores of MMSS at runtime, letting use them for the monitoring operation. This requires integrating them inside the production environments, i.e., where the actual MMSS are consumed by end-users. This aspect is often neglected by QoE modelers that focus mainly on the accuracy and fitness of their designed models with respect to a given set of settings and conditions. As a consequence, a considerable technical effort should be made in order to bring them from the laboratory to the production environments. This obviously discourages MMSS providers to easily accept and adopt them. For the sake of enhancing QoE models integration, we propose Mesukal, a software-layer ensuring portability of QoE models over a variety of underlying MMSS, e.g. YouTube or Netflix. Specifically, Mesukal acts as a Java Virtual Machine (JVM) enabling to build portable applications over different OS, e.g. Windows, Linux or MacOS. Mesukal can be considered as a virtual MMSS that is able to seamlessly interact with QoE models, on the one hand, and arbitrary real MMSS, on the other hand. Each considered MMSS over IP networks is appropriately virtualized by a dedicated Mesukal App. Besides real MMSS, Mesukal can be used to instantiate experimental MMSS where the accuracy and portability of QoE models may be inspected and checked under controlled conditions. The inputs needed by the concerned QoE models are fetched from each real MMSS using probes that are tailored following the technology used by the considered multimedia service. In addition, Mesukal includes a rich GUI dashboard that enables to inspect QoE results.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124612732","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

SYN Flood Attack Detection and Mitigation using Machine Learning Traffic Classification and Programmable Data Plane Filtering 基于机器学习流量分类和可编程数据平面过滤的SYN Flood攻击检测与缓解

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385540

Marinos Dimolianis, A. Pavlidis, B. Maglaris

{"title":"SYN Flood Attack Detection and Mitigation using Machine Learning Traffic Classification and Programmable Data Plane Filtering","authors":"Marinos Dimolianis, A. Pavlidis, B. Maglaris","doi":"10.1109/ICIN51074.2021.9385540","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385540","url":null,"abstract":"Distributed Denial of Service (DDoS) attacks are widely used by malicious actors to disrupt network infrastructures/services. A common attack is TCP SYN Flood that attempts to exhaust memory and processing resources. Typical mitigation mechanisms, i.e. SYN cookies require significant processing resources and generate large rates of backscatter traffic to block them. In this paper, we propose a detection and mitigation schema that focuses on generating and optimizing signature-based rules. To that end, network traffic is monitored and appropriate packet-level data are processed to form signatures i.e. unique combinations of packet field values. These are fed to machine learning models that classify them to malicious/benign. Malicious signatures corresponding to specific destinations identify potential victims. TCP traffic to victims is redirected to high-performance programmable XDPenabled firewalls that filter off ending traffic according to signatures classified as malicious. To enhance mitigation performance malicious signatures are subjected to a reduction process, formulated as a multi-objective optimization problem. Minimization objectives are (i) the number of malicious signatures and (ii) collateral damage on benign traffic. We evaluate our approach in terms of detection accuracy and packet filtering performance employing traces from production environments and high rate generated attack traffic. We showcase that our approach achieves high detection accuracy, significantly reduces the number of filtering rules and outperforms the SYN cookies mechanism in high-speed traffic scenarios.","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121512353","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

Assessing the carbon footprint of the data transmission on a backbone network 评估骨干网上数据传输的碳足迹

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385551

Marion Ficher, F. Berthoud, Anne-Laure Ligozat, Patrick Sigonneau, Maxime Wisslé, Badis Tebbani

引用次数: 3

A Multi-agent OpenAI Gym Environment for Telecom Providers Cooperation 面向电信运营商合作的多agent OpenAI Gym环境

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385538

Tangui Le Gléau, Xavier Marjou, Tayeb Lemlouma, Benoit Radier

{"title":"A Multi-agent OpenAI Gym Environment for Telecom Providers Cooperation","authors":"Tangui Le Gléau, Xavier Marjou, Tayeb Lemlouma, Benoit Radier","doi":"10.1109/ICIN51074.2021.9385538","DOIUrl":"https://doi.org/10.1109/ICIN51074.2021.9385538","url":null,"abstract":"The ever-increasing use of the Internet (streaming, Internet of things, etc.) constantly demands more connectivity, which incentivises telecommunications providers to collaborate by sharing resources to collectively increase the quality of service without deploying more infrastructure. However, to the best of our knowledge, there is no tool for testing and evaluating participation strategies in such collaborations. This article presents a new adaptable framework, based on the OpenAI Gym toolkit, allowing to generate customisable environments for cooperating on radio resources. This framework facilitates the development and comparison of agents (such as reinforcement learning agents) in a generic way. The main goal of the paper is to detail the available functionalities of our framework. We then focus on game theory aspects as multi-player games induced by these environments can be considered as sequential social dilemmas. We show in particular that although each agent has no incentive to remain cooperative at each step of such iterated games, a mutual cooperation provides better outcomes (in other words, Nash Equilibrium is non optimal)","PeriodicalId":347933,"journal":{"name":"2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117166664","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

Supporting VNF chains: an implementation using Segment Routing and PCEP 支持VNF链:使用段路由和PCEP的实现

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 2021-03-01 DOI: 10.1109/ICIN51074.2021.9385527

Cees Portegies, Marijke Kaat, P. Grosso

引用次数: 2

ICIN 2021 Additional Reviewers ICIN 2021新增审稿人

2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) Pub Date : 1900-01-01 DOI: 10.1109/icin51074.2021.9385555

引用次数: 0