Autonomic Defense of Distributed Information Security in Dynamic and Adversarial Environments

Abstract

The overarching goal of this research is to fill the current gap between available information flow assurance technologies and the n,eed for securing information flow in a Distributed, Dynamic, and aDversarial (referred to as D3) environment. To reach that goal, th,e central objective is to develop a new methodology for dynamic information flow security assurance, and a holistic framework that r,ealizes the methodology, that automatically senses, detects, and reacts to information flow security vulnerabilities and attacks in,a D3 environment at runtime. The key approach to realizing the objective is to tightly integrate scalable, cutting-edge code analysi,s (reasoning, transformation, and manipulation) techniques with novel machine learning models and algorithms tailored for working ef,fectively with software data and information flow security analysis tasks, so as to enable autonomy in defending the distributed inf,ormation infrastructure against adversaries.

Document Details

Document Type
DoD Grant Award
Publication Date
Feb 08, 2022
Source ID
N000142212111

Entities

People

  • Haipeng Cai

Organizations

  • Office of Naval Research
  • United States Navy
  • Washington State University

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Cybersecurity.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy