MIMETIC: Mobile encrypted traffic classification using multimodal deep learning
MIMETIC: Mobile encrypted traffic classification using multimodal deep learning
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DOI：
10.1016/j.comnet.2019.106944
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发表时间：
2019-12-24
影响因子：
5.6
通讯作者：
Pescape, Antonio
中科院分区：
文献类型：
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作者：
Aceto, Giuseppe;Ciuonzo, Domenico;Pescape, Antonio
移动流量分类(TC)如今已成为有价值的简档信息的推动者，而不是服务区分或阻止的主力。然而，设计准确的分类器的一个主要障碍是采用加密协议，这损害了深度数据包检测的有效性。此外，移动网络流量的不断变化的性质使得基于手动和专家发起的功能的机器学习(ML)解决方案无法跟上其步伐。这些限制为深度学习(DL)作为一种可行的策略扫清了道路，该策略基于自动提取的特征来设计流量分类器，反映了从多方面流量性质中提取的复杂模式，隐含地以“多模式”方式携带信息。TC中的多通道允许从互补的角度检查流量，从而为移动场景提供了有效的解决方案。因此，提出了一种新的加密TC的多通道下行链路框架MIMATIC，该框架能够充分利用业务数据的异构性(通过学习通道内和通道间的依赖关系)，克服现有(近视)基于单通道下行通道的TC方案的性能限制，并支持具有挑战性的移动场景。使用三个(人工生成的)移动加密流量数据集，我们展示了与(A)基于单通道DL的对应项、(B)最新的基于ML的(移动)流量分类器和(C)分类器融合技术相比，模拟的性能有所提高。(C)2019爱思唯尔B.V.保留所有权利。
Mobile Traffic Classification (TC) has become nowadays the enabler for valuable profiling information, other than being the workhorse for service differentiation or blocking. Nonetheless, a main hindrance in the design of accurate classifiers is the adoption of encrypted protocols, compromising the effectiveness of deep packet inspection. Also, the evolving nature of mobile network traffic makes solutions with Machine Learning (ML), based on manually- and expert-originated features, unable to keep its pace. These limitations clear the way to Deep Learning (DL) as a viable strategy to design traffic classifiers based on automatically-extracted features, reflecting the complex patterns distilled from the multifaceted traffic nature, implicitly carrying information in "multimodal" fashion. Multi-modality in TC allows to inspect the traffic from complementary views, thus providing an effective solution to the mobile scenario. Accordingly, a novel multimodal DL framework for encrypted TC is proposed, named MIMETIC, able to capitalize traffic data heterogeneity (by learning both intra- and inter-modality dependences), overcome performance limitations of existing (myopic) single-modality DL-based TC proposals, and support the challenging mobile scenario. Using three (human-generated) datasets of mobile encrypted traffic, we demonstrate performance improvement of MIMETIC over (a) single-modality DL-based counterparts, (b) state-of-theart ML-based (mobile) traffic classifiers, and (c) classifier fusion techniques. (C) 2019 Elsevier B.V. All rights reserved.