Abstract
Previous research has shown that properties of network traffic (network fingerprints) can be exploited to extract information about the content of streaming multimedia, even when the traffic is encrypted. However, the existing attacks suffer from several limitations: (i) the attack process is time consuming, (ii) the tests are performed under nearly identical network conditions while the practical fingerprints are normally variable in terms of the end-to-end network connections, and (iii) the total possible video streams are limited to a small pre-known set while the performance against possibly larger databases remains unclear. In this paper, we overcome the above limitations by introducing a traffic analysis scheme that is both robust and efficient for variable bit rate (VBR) video streaming. To construct unique and robust video signatures with different compactness, we apply a (wavelet-based) analysis to extract the long and short range dependencies within the video traffic. Statistical significance testing is utilized to construct an efficient matching algorithm. We evaluate the performance of the identification algorithm using a large video database populated with a variety of movies and TV shows. Our experimental results show that, even under different real network conditions, our attacks can achieve high detection rates and low false alarm rates using video clips of only a few minutes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Crotti, M., Gringoli, F., Pelosato, P., Salgarelli, L.: A statistical approach to IP-level classification of network traffic. In: IEEE International Conference on Communications, ICC 2006, vol. 1, pp. 170–176 (June 2006)
Okabe, T., Kitamura, T., Shizuno, T.: Statistical traffic identification method based on flow-level behavior for fair VoIP service. In: 1st IEEE Workshop on VoIP Management and Security, pp. 35–40 (April 2006)
Song, D.X., Wagner, D., Tian, X.: Timing analysis of keystrokes and timing attacks on SSH. In: Proc. the 10th Conference on USENIX Security Symposium, Berkeley, CA, USA, p. 25 (2001)
Canvel, B., Hiltgen, A., Vaudenay, S., Vuagnoux, M.: Password interception in a SSL/TLS channel. In: Boneh, D. (ed.) CRYPTO 2003. LNCS, vol. 2729, pp. 583–599. Springer, Heidelberg (2003)
Saponas, T.S., Lester, J., Hartung, C., Agarwal, S., Kohno, T.: Devices that tell on you: privacy trends in consumer ubiquitous computing. In: Proc. the 16th USENIX Security Symposium on USENIX Security Symposium, pp. 1–16 (2007)
Grunenfelder, R., Cosmas, J.P., Manthorpe, S., Odinma-Okafor, A.: Characterization of video codecs as autoregressive moving average. IEEE Journal on Selected Areas in Communication 9(3), 284–293 (1991)
Mallat, S.: A wavelet tour of signal processing. Academic Press, London (1998)
Schölkopf, B., Platt, J.C., Shawe-taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13, 1443–1471 (2001)
Video traces for network performance evaluation, http://trace.eas.asu.edu/
Liu, Y., Ou, C., Li, Z., Corbett, C., Mukherjee, B., Ghosal, D.: Wavelet-based traffic analysis for identifying video streams over broadband networks. In: IEEE Global Communications Conference, GLOBECOM 2008, pp. 1–6 (November 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Y., Sadeghi, AR., Ghosal, D., Mukherjee, B. (2011). Video Streaming Forensic – Content Identification with Traffic Snooping. In: Burmester, M., Tsudik, G., Magliveras, S., Ilić, I. (eds) Information Security. ISC 2010. Lecture Notes in Computer Science, vol 6531. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18178-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-18178-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18177-1
Online ISBN: 978-3-642-18178-8
eBook Packages: Computer ScienceComputer Science (R0)