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Network Traffic Analysis Using Stochastic Grammars, Chen Lu

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Network traffic analysis is widely used to infer information from Internet
traffic. This is possible even if the traffic is encrypted. Previous work uses
traffic characteristics, such as port numbers, packet sizes, and frequency,
without looking for more subtle patterns in the network traffic. In this work,
we use stochastic grammars, hidden Markov models (HMMs) and probabilistic
context-free grammars (PCFGs), as pattern recognition tools for traffic
analysis.
HMMs are widely used for pattern recognition and detection. We use a HMM
inference approach. With inferred HMMs, we use confidence intervals (CI) to
detect if a data sequence matches the HMM. To …