Paper Info
Title
Compressive Traffic Analysis: A New Paradigm for Scalable Traffic Analysis.
Abstract
Traffic analysis is the practice of inferring sensitive information from communication patterns, particularly packet timings and packet sizes. Traffic analysis is increasingly becoming relevant to security and privacy with the growing use of encryption and other evasion techniques that render content-based analysis of network traffic impossible. The literature has investigated traffic analysis for various application scenarios, from tracking stepping stone cybercriminals to compromising anonymity systems. The major challenge to existing traffic analysis mechanisms is scaling to today's exploding volumes of network traffic, i.e., they impose high storage, communications, and computation overheads. In this paper, we aim at addressing this scalability issue by introducing a new direction for traffic analysis, which we call \emph{compressive traffic analysis}. The core idea of compressive traffic analysis is to compress traffic features, and perform traffic analysis operations on such compressed features instead of on raw traffic features (therefore, improving the storage, communications, and computation overheads of traffic analysis due to using smaller numbers of features). To compress traffic features, compressive traffic analysis leverages linear projection algorithms from compressed sensing, an active area within signal processing. We show that these algorithms offer unique properties that enable compressing network traffic features while preserving the performance of traffic analysis compared to traditional mechanisms. We introduce the idea of compressive traffic analysis as a new generic framework for scalable traffic analysis. We then apply compressive traffic analysis to two widely studied classes of traffic analysis, namely, flow correlation and website fingerprinting. We show that the compressive versions of state-of-the-art flow correlation and website fingerprinting schemes\textemdash significantly\textemdash outperform their non-compressive (traditional) alternatives, e.g., the compressive version of Houmansadr et al. [44]'s flow correlation is two orders of magnitude faster, and the compressive version of Wang et al. [77] fingerprinting system runs about 13 times faster. We believe that our study is a major step towards scaling traffic analysis.
Year
DOI
Venue
2017
10.1145/3133956.3134074
CCS
Keywords
Field
DocType
Traffic analysis, compressed sensing, website fingerprinting, flow correlation
Traffic generation model,Internet privacy,Traffic analysis,Computer security,Computer science,Network packet,Encryption,Traffic shaping,Network traffic control,Network traffic simulation,Scalability
Conference
ISBN
Citations 
PageRank 
978-1-4503-4946-8
5
0.42
References 
Authors
51
3
Name
Order
Citations
PageRank
Milad Nasr Esfahani1927.74
Amir Houmansadr261442.27
Arya Mazumdar330741.81