Named Data Networking (NDN) is one of five projects funded by the U.S. National Science Foundation under its Future Internet Architecture Program. NDN has its roots in an earlier project, Content-Centric Networking (CCN), which Van Jacobson first publicly presented in 2006. The NDN project investigates Jacobson's proposed evolution from today's host-centric network architecture (IP) to a data-centric network architecture (NDN). This conceptually simple shift has far-reaching implications for how we design, develop, deploy, and use networks and applications. We describe the motivation and vision of this new architecture, and its basic components and operations. We also provide a snapshot of its current design, development status, and research challenges. More information about the project, including prototype implementations, publications, and annual reports, is available on named-data.net.

/pdf/named-data-networking-4kzv69blab.pdf

Named data networking

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

/pdf/a-comprehensive-survey-on-machine-learning-for-networking-3t5h3kbpe8.pdf

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Distributed Denial of Service (DDoS) attacks are an ongoing problem in today's Internet, where packets from a large number of compromised hosts thwart the paths to the victim site and/or overload the victim machines. In a newly proposed future Internet architecture, Named Data Networking (NDN), end users request desired data by sending Interest packets, and the network delivers Data packets upon request only, effectively eliminating many existing DDoS attacks. However, an NDN network can be subject to a new type of DDoS attack, namely Interest packet flooding. In this paper we investigate effective solutions to mitigate Interest flooding. We show that NDN's inherent properties of storing per packet state on each router and maintaining flow balance (i.e., one Interest packet retrieves at most one Data packet) provides the basis for effective DDoS mitigation algorithms. Our evaluation through simulations shows that the solution can quickly and effectively respond and mitigate Interest flooding.

https://cial.csie.ncku.edu.tw/presentation/group_pdf/(IFIP%202013)%20Interest%20Flooding%20Attack%20and%20Countermeasures%20in%20Named%20Data%20Networking.pdf

Interest flooding attack and countermeasures in Named Data Networking

Content-Centric Networking (CCN) is an emerging networking paradigm being considered as a possible replacement for the current IP-based host-centric Internet infrastructure. In CCN, named content becomes a first-class entity. CCN focuses on content distribution, which dominates current Internet traffic and is arguably not well served by IP. Named-Data Networking (NDN) is an example of CCN. NDN is also an active research project under the NSF Future Internet Architectures (FIA) program. FIA emphasizes security and privacy from the outset and by design. To be a viable Internet architecture, NDN must be resilient against current and emerging threats. This paper focuses on distributed denial-of-service (DDoS) attacks; in particular we address interest flooding, an attack that exploits key architectural features of NDN. We show that an adversary with limited resources can implement such attack, having a significant impact on network performance. We then introduce Poseidon: a framework for detecting and mitigating interest flooding attacks. Finally, we report on results of extensive simulations assessing proposed countermeasure.

Poseidon: Mitigating Interest Flooding DDoS Attacks in Named Data Networking

In this paper we apply the Named Data Networking [8], a newly proposed Internet architecture, to networking vehicles on the run Our initial design, dubbed V-NDN, illustrates NDN's promising potential in providing a unifying architecture that enables networking among all computing devices independent from whether they are connected through wired infrastructure, ad hoc, or intermittent DTN This paper describes a prototype implementation of V-NDN and its preliminary performance assessment, and identifies remaining challenges

/pdf/vanet-via-named-data-networking-1c2uvq8fh2.pdf

VANET via Named Data Networking

https://www.cs.arizona.edu/~yic/paper/comcom-forwarding.pdf

A case for stateful forwarding plane

NDN Forwarding Daemon (NFD) is a network forwarder that implements the Named Data Networking (NDN) protocol. NFD is designed with modularity and extensibility in mind to enable easy experiments with new protocol features, algorithms, and applications for NDN. To help developers extend and improve NFD, this document explains NFD’s internals including the overall design, major modules, their implementations, and their interactions.

/pdf/nfd-developer-s-guide-tmtyzocnj9.pdf

NFD Developer's Guide

Today's Internet applications and protocols are not compatible with Information Centric Networking (ICN) protocols and there is no straightforward way of rapidly switching protocol architectures. Network operators incrementally deploying an ICN infrastructure will have to provide compatibility with existing TCP/IP applications and manage co-existence of IP and ICN networks. One approach to co-existence is to allow TCP and the applications using it to work transparently over an ICN substrate instead of over IP. This paper presents a TCP/ICN proxy capable of carrying TCP traffic between TCP/IP endpoints over ICN network. The main challenge for this approach to co-existence is transforming the TCP push model to the ICN pull model. We evaluated several alternative TCP/ICN proxy designs in a simulation environment. We chose the most promising of these designs and developed a proof-of-concept *nix implementation. Performance measurements of both simulation and real implementation demonstrate that with our proxy design TCP can traverse ICN networks without significant additional delay or loss of goodput.

TCP/ICN: Carrying TCP over Content Centric and Named Data Networks

Named Data Networking (NDN) is a general purpose network layer protocol which offers a set of rich functionality: caching, multi-path forwarding, multicast delivery, and data-centric security. Above the network layer, system libraries simplify application developers' tasks by providing an easy to use yet powerful API to utilize the functions enabled by NDN. This paper presents the design of a Consumer / Producer programming interface, together with several mechanisms, that supports application level framing via NDN's data retrieval protocols to make NDN application programming easier and faster.

/pdf/consumer-producer-communication-with-application-level-yw4yqxouin.pdf

Consumer / Producer Communication with Application Level Framing in Named Data Networking

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