CTJIF-CN:信息中心网络中的辅助信任联合利益转发机制

Krishna Delvadia, N. Dutta
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引用次数: 0

摘要

以信息为中心的网络(ICN)通过允许内容驱动转发和网络内缓存机制,将当前互联网范式的焦点从以主机为中心转变为以数据为中心。虽然 ICN 的 NDN(命名数据网络)范例可确保内容通信的安全性,但它很容易受到恶意节点的各种攻击。为了最大限度地减少受攻击节点的危害并提高网络安全性,其余节点应透明地接收有关此类节点的信息。这将限制转发策略利用这些恶意节点转发兴趣和内容。我们的协议引入了一个用于预测信任度的动态模型,以评估节点信任度。拟议方法观察节点的历史行为,并使用扩展模糊逻辑规则预测未来行为,以评估节点的信任值。该预测模型被纳入基于信任的转发机制,旨在通过安全和最短路径转发兴趣。在 ns-3 驱动的 ndnSIM-2.0 模拟器中进行了广泛的模拟研究,分析了协议的性能指标,如数据发现延迟、数据包传送率、网络开销、检测率和缓存命中率。当我们将信任联合转发策略集成到最先进的协议中时,在现实网络拓扑条件下,这些协议的性能比既定性能指标显著提高了约 10-35%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CTJIF-ICN: A Coadjuvant Trust Joint Interest Forwarding Mechanism in Information Centric Networks
The Information centric networks (ICN) transforms the focal point of current Internet paradigm to data centric approach from host centric approach by allowing content driven forwarding and in-network caching mechanisms. Though NDN (Named data networking) paradigm of ICN assures a secure content communication, it is vulnerable to different attacks by the malicious nodes. To minimize the hazards from compromised nodes and to improve the network security, the remaining nodes should transparently receive information about such nodes. This will restrict the forwarding strategy to exploit these malicious nodes for forwarding interest and content as well. Our protocol introduces a dynamic model for prediction of trust in order to evaluate the node trust. Proposed approach observes the historical behaviors of node and uses extended fuzzy logic rules for the prediction of future behaviors to evaluate the node’s trust value. This prediction model is incorporated within the trust based forwarding mechanism that aims to forward interest through secure and shortest path. The extensive simulation study has been carried out to analyze the protocol performance in ns-3 driven ndnSIM-2.0 simulator for performance metrics such as data discovery latency, packet delivery ratio, network overhead, detection ratio and cache hit ratio. When we integrate our trust joint forwarding strategy to state-of-the-art protocols, their performance is significantly improved up to approximately 10-35% against stated performance measures for realistic network topology.
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