Sema-ICN: Toward Semantic Information-Centric Networking Supporting Smart Anomalous Access Detection

Abstract

As a next-generation networking architecture, information-centric networks (ICN) has strengthened the focus on physical location-independent content sharing, which introduces abundant semantic features and novel access approach. However, the semantic modeling of ICN is an unresolved problem, thus current ICN lacks the capabilities of smart content analysis and understanding to support the knowledge decision for optimized user experience. To address this issue, we propose a semantic ICN model, Sema-ICN, that can provide logically related information depending on content-relevance-based relationships extraction and name-based weight setting. Moreover, besides the great benefits brought into ICN by semantic features, Sema- ICN will also contribute to security protection against anomalous access, which usually the basis of further threats. In this paper, we additionally design a smart anomalous access detection scheme supported by Sema- ICN, in which semantic communities are partitioned utilizing spectral clustering according to content name with semantic attributes. And a forecast model is introduced to predict access situation based on triple exponential smoothing algorithm using historical request data, access traffic that is beyond the forecast results will be considered as anomalous. The simulation results demonstrate the efficiency of the proposed scheme. To the best of our knowledge, this work is the first to propose a novel semantic model for ICN.