SDN and NFV as Enabler for the Distributed Network Cloud

Hoffmann, Marco; Jarschel, Michael; Pries, Rastin; Schneider, Peter; Jukan, Admela; Bziuk, Wolfgang; Gebert, Steffen; Zinner, Thomas; Tran-Gia, Phuoc

doi:10.1007/s11036-017-0905-y

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…Sudden migration from current traditional networks to SDN and NFV networks is practically difficult to process due to lack of adaptability and flexibility in the current network. Rather, it is better to take a step-by-step migration process [ 13 ]. Network services are configured as a software program in hardware element as VNFs, physical hardware resources for VNFs can be configured and migrated dynamically over a multi-domain network [ 14 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Prabakaran

Ramalakshmi

Hussain

et al. 2022

Sensors

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Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller’s role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native–Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.

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Section: Background and Related Workmentioning

confidence: 99%

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Prabakaran

Ramalakshmi

Hussain

et al. 2022

Sensors

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“…They have appropriately considered different risks, such as DDoS attacks, to the addressed program-the performance analytics component not adequately organized to calculate the various parameters specified in the networks. Hoffmann et al concentrated on pushing mobile network development towards costefficient IT-based solutions utilizing standardized hardware and software-based thoughts such as SDN and NFV [22].…”

Section: ) Iot-sdn With Nfvmentioning

confidence: 99%

DistB-Condo: Distributed Blockchain-Based IoT-SDN Model for Smart Condominium

et al. 2020

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Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined Network (SDN), Network Function Virtualization (NFV) and Blockchain (BC). These technologies provide a robust, and secured platform to meet all kinds of challenges, such as safety, confidentiality, flexibility, efficiency, and availability. This work suggests a distributed, scalable IoT-SDN with Blockchain-based NFV framework for a smart condominium (DistB-Condo) that can act as an efficient secured platform for a small community. Moreover, the Blockchain-based IoT-SDN with NFV framework provides the combined benefits of leading technologies. It also presents an optimized Cluster Head Selection (CHS) algorithm for selecting a Cluster Head (CH) among the clusters that efficiently saves energy. Besides, a decentralized and secured Blockchain approach has been introduced that allows more prominent security and privacy to the desired condominium network. Our proposed approach has also the ability to detect attacks in an IoT environment. Eventually, this article evaluates the performance of the proposed architecture using different parameters (e.g., throughput, packet arrival rate, and response time). The proposed approach outperforms the existing OF-Based SDN. DistB-Condo has better throughput on average, and the bandwidth (Mbps) much higher than the OF-Based SDN approach in the presence of attacks. Also, the proposed model has an average response time of 5% less than the core model.

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“…Numerous areas of networking are targeted by the NFV paradigm [1], [2]. These range from traditional middleboxes [3] like NATs and firewalls to mobile network functions [4] such as Serving or Packet Gateways (SGW, PGW) and Mobile Management Entities (MME), as well as further extending to customer premises equipment and media transcoders. In this section, we provide a structured overview of the NFV ecosystem in order to cope with the resulting heterogeneity in terms of requirements for an efficient operation of individual functions as well as management of their coexistence on a shared physical substrate.…”

Section: Network Function Virtualization Ecosystemmentioning

confidence: 99%

“…As outlined above, there are numerous different VNF types and areas of use. Specialized surveys and articles provide information regarding individual functions and their details [1], [2], [4], [5]. In contrast, we first (1) identify common building blocks that can be used to compose and classify a particular function.…”

Section: A Set Of Network Functions and Building Blocksmentioning

confidence: 99%

Survey of Performance Acceleration Techniques for Network Function Virtualization

et al. 2019

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The ongoing network softwarization trend holds the promise to revolutionize network infrastructures by making them more flexible, reconfigurable, portable, and more adaptive than ever. Still, the migration from hard-coded/hardwired network functions towards their software-programmable counterparts comes along with the need for tailored optimizations and acceleration techniques, so as to avoid, or at least mitigate, the throughput/latency performance degradation with respect to fixed function network elements. The contribution of this article is twofold. First, we provide a comprehensive overview of the host-based Network Function Virtualization (NFV) ecosystem, covering a broad range of techniques, from low level hardware acceleration and bump-in-the-wire offloading approaches, to highlevel software acceleration solutions, including the virtualization technique itself. Second, we derive guidelines regarding the design, development, and operation of NFV-based deployments that meet the flexibility and scalability requirements of modern communication networks.

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SDN and NFV as Enabler for the Distributed Network Cloud

Cited by 16 publications

References 14 publications

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

DistB-Condo: Distributed Blockchain-Based IoT-SDN Model for Smart Condominium

Survey of Performance Acceleration Techniques for Network Function Virtualization

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