Using Machine Learning in Adversarial Environments
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Vanderbilt Univ., Nashville, TN (United States)
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.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000;
- OSTI ID:
- 1563076
- Report Number(s):
- SAND-2016-10426; 648349
- Country of Publication:
- United States
- Language:
- English
Similar Records
Using Machine Learning in Adversarial Environments.
Automated Adversary-in-the-Loop Cyber-Physical Defense Planning
Defending Against Adversarial Examples
Technical Report
·
2016
·
OSTI ID:1238101
Automated Adversary-in-the-Loop Cyber-Physical Defense Planning
Journal Article
·
2023
· ACM Transactions on Cyber-Physical Systems
·
OSTI ID:2228580
Defending Against Adversarial Examples
Technical Report
·
2019
·
OSTI ID:1569514