2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)最新文献_第3页

Network Slicing Over A Packet/Optical Network For Vertical Applications Applied To Multimedia Real-Time Communications 面向多媒体实时通信垂直应用的分组/光网络网络切片

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/NFV-SDN47374.2019.9040062

P. Alemany, Thomas Soenen, Carlos Parada, José Bonnet, R. Casellas, R. Martínez, R. Vilalta, R. Muñoz, J. D. L. Cruz, Ana Pol, Antón Román, P. Trakadas, P. Karkazis, M. Touloupou, E. Kapassa, D. Kyriazis

引用次数: 8

Flock: A Live Migration Protocol for SDN Controllers Flock: SDN控制器的热迁移协议

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/NFV-SDN47374.2019.9040146

C. Contoli, Fabio Palumbo, Flavio Esposito, F. Callegati, A. Pescapé

引用次数: 0

Investigating Adversarial Attacks against Network Intrusion Detection Systems in SDNs 研究针对sdn网络入侵检测系统的对抗性攻击

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/NFV-SDN47374.2019.9040101

James Aiken, Sandra Scott-Hayward

{"title":"Investigating Adversarial Attacks against Network Intrusion Detection Systems in SDNs","authors":"James Aiken, Sandra Scott-Hayward","doi":"10.1109/NFV-SDN47374.2019.9040101","DOIUrl":"https://doi.org/10.1109/NFV-SDN47374.2019.9040101","url":null,"abstract":"Machine-learning based network intrusion detection systems (ML-NIDS) are increasingly popular in the fight against network attacks. In particular, promising detection results have been demonstrated in conjunction with Software-Defined Networks (SDN), in which the logically centralized control plane provides access to data from across the network. However, research into adversarial attacks against machine learning classifiers has highlighted vulnerabilities in a number of fields. These vulnerabilities raise concerns about the implementation of similar classifiers in anomaly-based NIDSs within SDNs. In this work, we investigate the viability of adversarial attacks against classifiers in this field. We implement an anomaly-based NIDS, Neptune, as a target platform that utilises a number of different machine learning classifiers and traffic flow features. We develop an adversarial test tool, Hydra, to evaluate the impact of adversarial evasion classifier attacks against Neptune with the goal of lowering the detection rate of malicious network traffic. The results demonstrate that with the perturbation of a few features, the detection accuracy of a specific SYN flood Distributed Denial of Service (DDoS) attack by Neptune decreases from 100% to 0% across a number of classifiers. Based on these results, recommendations are made as to how to increase the robustness of classifiers against the demonstrated attacks.","PeriodicalId":394933,"journal":{"name":"2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127993448","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: 39

Towards Novel Security Architectures for Network Functions Virtualization 面向网络功能虚拟化的新型安全架构

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/NFV-SDN47374.2019.9040068

M. Repetto, A. Carrega, G. Lamanna

{"title":"Towards Novel Security Architectures for Network Functions Virtualization","authors":"M. Repetto, A. Carrega, G. Lamanna","doi":"10.1109/NFV-SDN47374.2019.9040068","DOIUrl":"https://doi.org/10.1109/NFV-SDN47374.2019.9040068","url":null,"abstract":"The definition of elastic network services that can be orchestrated at run-time brings unprecedented agility and dynamicity in network operation, but also complicates security management. As a matter of fact, cyber-security appliances are still largely stuck to traditional paradigms, based on relatively static topologies and the security perimeter model. The uptake of service-oriented architectures and microservices is now suggesting to compose security services by orchestrating monitoring, inspection, and enforcement capabilities, which are natively implemented in each elementary component (virtual functions, software-defined network equipment). In this paper, we describe and evaluate a novel framework for monitoring, inspection and enforcement that provides a broad and heterogeneous security context for centralized analytics, correlation and detection. Our work represents the preliminary step towards the creation of true Security-as-a-Service (SecaaS) paradigms in virtualized environments, through programmatic composition of common capabilities available in each virtual function.","PeriodicalId":394933,"journal":{"name":"2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121699110","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

Anomalous Rule Detection using Machine Learning in Software Defined Networks 软件定义网络中使用机器学习的异常规则检测

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/NFV-SDN47374.2019.9039984

Vignesh Sridharan, G. Mohan, A. Leon-Garcia

{"title":"Anomalous Rule Detection using Machine Learning in Software Defined Networks","authors":"Vignesh Sridharan, G. Mohan, A. Leon-Garcia","doi":"10.1109/NFV-SDN47374.2019.9039984","DOIUrl":"https://doi.org/10.1109/NFV-SDN47374.2019.9039984","url":null,"abstract":"The centralized control plane in Software Defined Networking (SDN) introduces new security threats to the network. A compromised controller can install malicious rules at the switches to perform stealthy attacks such as intermittent packet dropping, route misdirection etc. Replication based approaches in the literature require the switches to broadcast the requests to multiple controllers and verify the rules for consistency before installing them. However, they result in heavy load on the control plane and longer response time for requests from the switches. Other approaches assume forwarding elements, rather than the controller, to be compromised and propose packet sampling and active probing to identify malicious behavior. In this work, we: i) propose a machine learning based framework to detect anomalous behavior at the flow table and identify the compromised controller, ii) develop MTADS, a M achine learning based detection T echnique for A nomaly D etection in S DN, which uses D BSCAN algorithm to identify anomalous rules and behavior, and iii) implement MTADS on top of Floodlight controller managing a network emulated in Mininet and test its detection capabilities against various attacks such as route misdirection, packet drop etc. We compare the performance of MTADS (based on DBSCAN) with K-Means algorithm and show that MTADS (DBSCAN) outperforms the K-Means version and achieves precision and recall of about 85% and 95&, respectively.","PeriodicalId":394933,"journal":{"name":"2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114589913","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: 1

NFV-SDN 2019 Welcome NFV-SDN 2019欢迎

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/nfv-sdn47374.2019.9040107

引用次数: 0

Flexible Notification Forwarding for Content-Based Publish/Subscribe Using P4 使用P4实现基于内容的发布/订阅的灵活通知转发

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/NFV-SDN47374.2019.9040048

Christian Wernecke, Helge Parzyjegla, Gero Mühl, E. Schweissguth, D. Timmermann

引用次数: 3

Enhanced Driving with 5G: A New Approach for Alleviating Traffic Congestion 5G增强驾驶:缓解交通拥堵的新途径

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/NFV-SDN47374.2019.9040009

Roberto Torre Arranz, Goiuri Peralta, Oleksandr Zhdanenko, Alexander Kropp, H. Salah, Giang T. Nguyen, S. Mudriievskyi, F. Fitzek

引用次数: 0

Deep Reinforcement Learning for Topology-Aware VNF Resource Prediction in NFV Environments NFV环境下拓扑感知VNF资源预测的深度强化学习

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/NFV-SDN47374.2019.9040154

Nikita Jalodia, Shagufta Henna, A. Davy

{"title":"Deep Reinforcement Learning for Topology-Aware VNF Resource Prediction in NFV Environments","authors":"Nikita Jalodia, Shagufta Henna, A. Davy","doi":"10.1109/NFV-SDN47374.2019.9040154","DOIUrl":"https://doi.org/10.1109/NFV-SDN47374.2019.9040154","url":null,"abstract":"Network Function Virtualisation (NFV) has emerged as a key paradigm in network softwarisation, enabling virtualisation in future generation networks. Once deployed, the Virtual Network Functions (VNFs) in an NFV application’s Service Function Chain (SFC) experience dynamic fluctuations in network traffic and requests, which necessitates dynamic scaling of resource instances. Dynamic resource management is a critical challenge in virtualised environments, specifically while balancing the trade-off between efficiency and reliability. Since provisioning of virtual infrastructures is time-consuming, this negates the Quality of Service (QoS) requirements and reliability criterion in latency-critical applications such as autonomous driving. This calls for predictive scaling decisions to balance the provisioning time sink, with a methodology that preserves the topological dependencies between the nodes in an SFC for effective resource forecasting. To address this, we propose the model for an Asynchronous Deep Reinforcement Learning (DRL) enhanced Graph Neural Networks (GNN) for topology-aware VNF resource prediction in dynamic NFV environments.","PeriodicalId":394933,"journal":{"name":"2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)","volume":"67 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120838972","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: 18

Traffic-aware Live Migration in Virtualized CPE Scenarios 虚拟化CPE场景下的流量感知热迁移

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Pub Date : 2019-11-01 DOI: 10.1109/NFV-SDN47374.2019.9039975

Flávio Meneses, M. Fernandes, T. Vieira, Daniel Corujo, Sérgio Figueiredo, A. Neto, R. Aguiar

引用次数: 2