Efective and Real-time In-App Activity Analysis in Encrypted Internet Traffic Streams

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

41 Scopus Citations
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Author(s)

  • Yanjie Fu
  • Jingci Ming
  • Yong Ren
  • Leilei Sun
  • Hui Xiong

Detail(s)

Original languageEnglish
Title of host publicationKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherACM New York
Pages335-344
Number of pages10
ISBN (Electronic)9781450348874
Publication statusPublished - 13 Aug 2017
Externally publishedYes

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F129685

Conference

Title23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017)
PlaceCanada
CityHalifax
Period13 - 17 August 2017

Abstract

The mobile in-App service analysis, aiming at classifying mobile internet trafic into different types of service usages, has become a challenging and emergent task for mobile service providers due to the increasing adoption of secure protocols for in-App services. While some efforts have been made for the classification of mobile internet trafic, existing methods rely on complex feature construction and large storage cache, which lead to low processing speed, and thus not practical for online real-time scenarios. To this end, we develop an iterative analyzer for classifying encrypted mobile trafic in a real-time way. Specifically, we first select an optimal set of most discriminative features from raw features extracted from trafic packet sequences by a novel Maximizing Inner activity similarity and Minimizing Different activity similarity (MIMD) measurement. To develop the online analyzer, we first represent a trafic flow with a series of time windows, which are described by the optimal feature vector and are updated iteratively at the packet level. Instead of extracting feature elements from a series of raw trafic packets, our feature elements are updated when a new trafic packet is observed and the storage of raw trafic packets is not required. The time windows generated from the same service usage activity are grouped by our proposed method, namely, recursive time continuity constrained KMeans clustering (rCKC). The feature vectors of cluster centers are then fed into a random forest classifier to identify corresponding service usages. Finally, we provide extensive experiments on real-world trafic data from Wechat, Whatsapp, and Facebook to demonstrate the effectiveness and eficiency of our approach. The results show that the proposed analyzer provides high accuracy in real-world scenarios, and has low storage cache requirement as well as fast processing speed.

Research Area(s)

  • In-app analytics, Internet trafic analysis, Service usage classification, Time series segmentation

Citation Format(s)

Efective and Real-time In-App Activity Analysis in Encrypted Internet Traffic Streams. / Liu, Junming; Fu, Yanjie; Ming, Jingci et al.
KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM New York, 2017. p. 335-344 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F129685).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review