Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data
This work addresses pattern detection in stochastic systems for domains like security and logistics, but it appears incremental as it builds on existing Markov model and statistical methods.
The authors tackled the problem of extracting stochastic state machines (Markov models) from raw data to detect patterns, with applications including inferring shipping patterns, exploiting side-channel information, and detecting botnet activities, though no concrete numerical results are provided.
Many systems are partially stochastic in nature. We have derived data driven approaches for extracting stochastic state machines (Markov models) directly from observed data. This chapter provides an overview of our approach with numerous practical applications. We have used this approach for inferring shipping patterns, exploiting computer system side-channel information, and detecting botnet activities. For contrast, we include a related data-driven statistical inferencing approach that detects and localizes radiation sources.