tailieunhanh - On Inferring Application Protocol Behaviors in Encrypted Network Traffic

Enterprise IT and security professionals are being challenged to defend against increasingly complex cyber attacks on their businesses. However, in most cases, they still rely on the tools of "yesterday" to get the work done. In many cases, due to the restraints of reduced security-oriented staff and limited and tight budgets, security managers continue to use what they have always used, even if it isn't totally effective. It is interesting to note that in IDC's Enterprise Security Surveys, the overall confidence of respondents in their enterprise security has fallen from 61% in 2004 to 46% in 2008; however, the. | Journal of Machine Learning Research 7 2006 2745-2769 Submitted 3 06 Revised 9 06 Published 12 06 On Inferring Application Protocol Behaviors in Encrypted Network Traffic Charles V. Wright Fabian Monrose Gerald M. Masson Information Security Institute Johns Hopkins University Baltimore MD 21218 USA CVWRIGHT@ FABIAN@ MASSON@ Editor Philip Chan Abstract Several fundamental security mechanisms for restricting access to network resources rely on the ability of a reference monitor to inspect the contents of traffic as it traverses the network. However with the increasing popularity of cryptographic protocols the traditional means of inspecting packet contents to enforce security policies is no longer a viable approach as message contents are concealed by encryption. In this paper we investigate the extent to which common application protocols can be identified using only the features that remain intact after encryption namely packet size timing and direction. We first present what we believe to be the first exploratory look at protocol identification in encrypted tunnels which carry traffic from many TCP connections simultaneously using only post-encryption observable features. We then explore the problem of protocol identification in individual encrypted TCP connections using much less data than in other recent approaches. The results of our evaluation show that our classifiers achieve accuracy greater than 90 for several protocols in aggregate traffic and for most protocols greater than 80 when making fine-grained classifications on single connections. Moreover perhaps most surprisingly we show that one can even estimate the number of live connections in certain classes of encrypted tunnels to within on average better than 20 . Keywords traffic classification hidden Markov models network security 1. Introduction To effectively manage large networks an administrator s ability to characterize the traffic within the network s boundaries is critical .

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