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Network Traffic Analysis Using Stochastic Grammars, Chen Lu
Network Traffic Analysis Using Stochastic Grammars, Chen Lu
All Dissertations
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 …
Investigation Of Training Algorithms For Hidden Markov Models Applied To Automatic Speech Recognition, Eric Fang
All Theses
The work presented in this thesis focuses on simulating a speech recognizer which is trained by different people with different speaking styles and investigates how sensitive the training and recognition processes are to the variations in the training data. There are four main parts to this work. The first involves an experiment of weighting methods for training with multiple observation sequences. The second involves the testing of different initial parameters. The third part includes the first experiment involving training with multiple observation sequences. The model's sensitivity to variations in training data was evaluated by comparing the cases of different values …