Virtualizing Network Services: Practical Perspectives

2017 ◽  
Vol 2 (2) ◽  
pp. 470-478
Author(s):  
Emad H. Al-Hemiary
Keyword(s):  
2021 ◽  
pp. 101412
Author(s):  
Vitor A. Cunha ◽  
Daniel Corujo ◽  
Joao P. Barraca ◽  
Rui L. Aguiar

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1342
Author(s):  
Borja Nogales ◽  
Miguel Silva ◽  
Ivan Vidal ◽  
Miguel Luís ◽  
Francisco Valera ◽  
...  

5G communications have become an enabler for the creation of new and more complex networking scenarios, bringing together different vertical ecosystems. Such behavior has been fostered by the network function virtualization (NFV) concept, where the orchestration and virtualization capabilities allow the possibility of dynamically supplying network resources according to its needs. Nevertheless, the integration and performance of heterogeneous network environments, each one supported by a different provider, and with specific characteristics and requirements, in a single NFV framework is not straightforward. In this work we propose an NFV-based framework capable of supporting the flexible, cost-effective deployment of vertical services, through the integration of two distinguished mobile environments and their networks: small sized unmanned aerial vehicles (SUAVs), supporting a flying ad hoc network (FANET) and vehicles, promoting a vehicular ad hoc network (VANET). In this context, a use case involving the public safety vertical will be used as an illustrative example to showcase the potential of this framework. This work also includes the technical implementation details of the framework proposed, allowing to analyse and discuss the delays on the network services deployment process. The results show that the deployment times can be significantly reduced through a distributed VNF configuration function based on the publish–subscribe model.


2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.


2021 ◽  
Vol 13 (3) ◽  
pp. 1522
Author(s):  
Raja Majid Ali Ujjan ◽  
Zeeshan Pervez ◽  
Keshav Dahal ◽  
Wajahat Ali Khan ◽  
Asad Masood Khattak ◽  
...  

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.


2001 ◽  
Vol 26 (3) ◽  
pp. 8-13 ◽  
Author(s):  
Aniko L. Halverson ◽  
Joye Volker

Two libraries, the National Institute of the Arts at the Australian National University and California Institute of the Arts in Southern California, describe how each has an interdependent relationship with the information technology or network services units in their respective institutions. Major considerations for both are the introduction of electronic full-text art information on the Web and its pedagogical implications, issues faced by arts libraries in the integration of computer services with library services in universities and colleges, and the changing roles of arts librarians and libraries.


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