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Monitoring and Counter-Profiling for Voice over IP Networks and Services

Identifieur interne : 003658 ( Main/Merge ); précédent : 003657; suivant : 003659

Monitoring and Counter-Profiling for Voice over IP Networks and Services

Auteurs : Rémi Badonnel [France] ; Olivier Festor [France] ; Khaled Hamlaoui [France]

Source :

RBID : Hal:hal-00406537

English descriptors

Abstract

Voice over IP (VoIP) has become a major paradigm for providing lower operational costs and higher flexibility in networks and services. VoIP infrastructures are however facing multiple security issues. In particular, monitoring methods and techniques can be applied to VoIP traffic in order to profile and track network users. We present in this paper a counter-measure strategy for preventing VoIP profiling.We propose two functional architectures with different noise generation functions in order to dynamically generate fake VoIP messages and deteriorate the profiling performances. We quantify the benefits and limits of our approach through an implementation prototype and the analysis of experimental results obtained in the case scenario of profiling methods based on principal component analysis (PCA).

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Hal:hal-00406537

Le document en format XML

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<front>
<div type="abstract" xml:lang="en">Voice over IP (VoIP) has become a major paradigm for providing lower operational costs and higher flexibility in networks and services. VoIP infrastructures are however facing multiple security issues. In particular, monitoring methods and techniques can be applied to VoIP traffic in order to profile and track network users. We present in this paper a counter-measure strategy for preventing VoIP profiling.We propose two functional architectures with different noise generation functions in order to dynamically generate fake VoIP messages and deteriorate the profiling performances. We quantify the benefits and limits of our approach through an implementation prototype and the analysis of experimental results obtained in the case scenario of profiling methods based on principal component analysis (PCA).</div>
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