4,790 research outputs found
Optical Network Models and their Application to Software-Defined Network Management
Software-defined networking is finding its way into optical networks. Here,
it promises a simplification and unification of network management for optical
networks allowing automation of operational tasks despite the highly diverse
and vendor-specific commercial systems and the complexity and analog nature of
optical transmission. A fundamental component for software-defined optical
networking are common abstractions and interfaces. Currently, a number of
models for optical networks are available. They all claim to provide open and
vendor agnostic management of optical equipment. In this work, we survey and
compare the most important models and propose an intent interface for creating
virtual topologies that is integrated in the existing model ecosystem.Comment: Parts of the presented work has received funding from the European
Commission within the H2020 Research and Innovation Programme, under grant
agreeement n.645127, project ACIN
SDN Access Control for the Masses
The evolution of Software-Defined Networking (SDN) has so far been
predominantly geared towards defining and refining the abstractions on the
forwarding and control planes. However, despite a maturing south-bound
interface and a range of proposed network operating systems, the network
management application layer is yet to be specified and standardized. It has
currently poorly defined access control mechanisms that could be exposed to
network applications. Available mechanisms allow only rudimentary control and
lack procedures to partition resource access across multiple dimensions.
We address this by extending the SDN north-bound interface to provide control
over shared resources to key stakeholders of network infrastructure: network
providers, operators and application developers. We introduce a taxonomy of SDN
access models, describe a comprehensive design for SDN access control and
implement the proposed solution as an extension of the ONOS network controller
intent framework
Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats
Network steganography is the art of hiding secret information within innocent
network transmissions. Recent findings indicate that novel malware is
increasingly using network steganography. Similarly, other malicious activities
can profit from network steganography, such as data leakage or the exchange of
pedophile data. This paper provides an introduction to network steganography
and highlights its potential application for harmful purposes. We discuss the
issues related to countering network steganography in practice and provide an
outlook on further research directions and problems.Comment: 11 page
De-ossifying the Internet Transport Layer : A Survey and Future Perspectives
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their useful suggestions and comments.Peer reviewedPublisher PD
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
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