Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data
Cryptography and Security
2018-06-26 v1
Abstract
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.
Cite
@article{arxiv.1709.07573,
title = {Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data},
author = {Richard R. Brooks and Lu Yu and Yu Fu and Guthrie Cordone and Jon Oakley and Xingsi Zhong},
journal= {arXiv preprint arXiv:1709.07573},
year = {2018}
}
Comments
Accepted by 2017 International Symposium on Sensor Networks, Systems and Security