Secure naming in information-centric networks

Wong, William G.; Nikander, Pekka

doi:10.1145/1921233.1921248

Cited by 35 publications

(24 citation statements)

References 14 publications

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“…At the same time however, apart from the frequent communication of the updated service class values to the content provider, the transient character of service class values would possibly also necessitate the frequent reconfiguration of caches so as to enable the identification of premium content. A potential approach to address these issues relies on the use of algorithmic identifiers [8], [2]. Instead of ISPs communicating each new ephemeral value of the service class identifier, they can communicate an algorithm through which content providers may produce subsequent ephemeral values themselves.…”

Section: B Handling Service Class Informationmentioning

confidence: 99%

“…Generally speaking, content in the ICN area is defined in terms of chunks, which need to be routable and self-certifying [1], [2]. The content name or identifier is then used to route contents, store them and look them up in innetwork caches (i.e., not only in overlay, hierarchical caches or CDNs) and validate their origin.…”

Section: Introductionmentioning

confidence: 99%

“…• Security: design names that allow for easy verification of content objects integrity, as well as for authentication of the content source [2], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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On information exposure through named content

Κατσαρός

Saino

Psaras

et al. 2014

10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

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Abstract-The proposed shift from host-centric to information-centric networking (ICN) has triggered extensive research in the area of content naming. Efforts have so far focused on the scalability and security properties that can make content objects routable and self-certifying. In this paper, we argue that the information that is exposed through explicitly naming content objects has been overlooked, although several operational and performance issues depend on the information that a name holds. We therefore revisit content naming design decisions taking into account information exposure and deployability of the ICN paradigm.

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Section: B Handling Service Class Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On information exposure through named content

Κατσαρός

Saino

Psaras

et al. 2014

10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

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“…A content can also be spoofed by injecting fake responses that are not signed or are signed with a wrong key, hoping that the user accepts the response in source. An old content (which may be unsecured) signed with the right key can be also replaced with the original one, or an attacker may get high access to the source's signing key to sign content with the correct key [39].…”

Section: Dos Attacks In Ccnmentioning

confidence: 99%

Data Clustering for Anomaly Detection in Content-Centric Networks

Karami¹

2013

IJCA

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Content-Centric Networks (CCNs) have recently emerged as an innovative trend to overcome many inherent security problems in the IP-based (host-based) networks by securing the content itself rather than the channel through which it travels. In this network architecture new kinds of attacks -ranging from DoS to privacy attacks-will appear. Therefore, it is becoming necessary to design a flexible and powerful mechanism to be able to detect them in an intelligent manner the first time they are employed. In this paper, a novel anomaly detection system has been proposed to detect known and previously unknown types of attacks using an efficient unsupervised learning engine that utilizes clustering with the optimal number of clusters, high detection rate, and low false positive rate in the same time over the CCN traffics flows. This paper compares the performance of five different clustering algorithms in the proposed anomaly detection system including K-means and Farthest First as Partitioning clustering, Cobweb as Hierarchical clustering, DBSCAN as Density-based clustering and Self Organizing Map (SOM) as Model-based clustering. Results show that DBSCAN method is the most efficient one for this purpose since it outperforms the other ones in terms of high detection rate and low false positive rate in the same time.General Terms:

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“…The NetInf design in [1] is more focused on the session layer in ICN: object search and name-resolution while the transport layer is currently studied in the SAIL project. Recent works, evaluating aspects of ICN architectures, explore content router issues [3], secure naming [14], [8] as well as congestion control [2]. Finally, an experimental evaluation of chunk-level caching in CCN is presented in [6].…”

Section: Introductionmentioning

confidence: 99%

Bandwidth and storage sharing performance in information centric networking

Muscariello

Carofiglio²,

Gallo

2011

Proceedings of the ACM SIGCOMM Workshop on Information-Centric Networking

130

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Internet usage has dramatically evolved towards content dissemination and retrieval, whilst the underlying infrastructure remains tied up to hosts interconnection. Information centric networking (ICN) proposals have recently emerged to rethink Internet foundations and design a natively contentcentric network environment.Important features of such networks are the availability of built-in network storage and of receiver-driven chunklevel transport, whose interaction significantly impacts overall system and user performance. In the paper, we provide an analytical characterization of statistical bandwidth and storage sharing, under fairly general assumption on total demand, topology, content popularity and limited network resources. A closed-form expression for average content delivery time is derived and its accuracy confirmed by eventdriven simulations. Finally, we present some applications of our model, leveraging on explicit formulae for the optimal dimensioning and localization of storage resources.

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Secure naming in information-centric networks

Cited by 35 publications

References 14 publications

On information exposure through named content

On information exposure through named content

Data Clustering for Anomaly Detection in Content-Centric Networks

Bandwidth and storage sharing performance in information centric networking

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