Publications

Results 26–50 of 105

Search results

Jump to search filters

Using Machine Learning in Adversarial Environments

Davis, Warren L.; Dunlavy, Daniel D.; Vorobeychik, Yevgeniy; Butler, Karin B.; Forsythe, Chris; Letter, Matthew L.; Murchison, Nicole M.; Nauer, Kevin S.

Cyber defense is an asymmetric battle today. We need to understand better what options are available for providing defenders with possible advantages. Our project combines machine learning, optimization, and game theory to obscure our defensive posture from the information the adversaries are able to observe. The main conceptual contribution of this research is to separate the problem of prediction, for which machine learning is used, and the problem of computing optimal operational decisions based on such predictions, coupled with a model of adversarial response. This research includes modeling of the attacker and defender, formulation of useful optimization models for studying adversarial interactions, and user studies to measure the impact of the modeling approaches in realistic settings.

More Details
TYPE SAND Report YEAR 2016
DOIOSTI

Constrained Versions of DEDICOM for Use in Unsupervised Part-Of-Speech Tagging

Dunlavy, Daniel D.; Chew, Peter A.

This reports describes extensions of DEDICOM (DEcomposition into DIrectional COMponents) data models [3] that incorporate bound and linear constraints. The main purpose of these extensions is to investigate the use of improved data models for unsupervised part-of-speech tagging, as described by Chew et al. [2]. In that work, a single domain, two-way DEDICOM model was computed on a matrix of bigram fre- quencies of tokens in a corpus and used to identify parts-of-speech as an unsupervised approach to that problem. An open problem identi ed in that work was the com- putation of a DEDICOM model that more closely resembled the matrices used in a Hidden Markov Model (HMM), speci cally through post-processing of the DEDICOM factor matrices. The work reported here consists of the description of several models that aim to provide a direct solution to that problem and a way to t those models. The approach taken here is to incorporate the model requirements as bound and lin- ear constrains into the DEDICOM model directly and solve the data tting problem as a constrained optimization problem. This is in contrast to the typical approaches in the literature, where the DEDICOM model is t using unconstrained optimization approaches, and model requirements are satis ed as a post-processing step.

More Details
TYPE SAND Report YEAR 2016
DOIOSTI
Results 26–50 of 105
Results 26–50 of 105