Perpetual Model Validation

Abstract

This research effort investigated fundamental techniques to provide perpetual model validation, where design-time models are validated continuously during runtime. The research considered two fronts: validation of the software model, and validation of the model of the system interacting with the physical world. Since the field of runtime verification has extensively focused on the challenge of runtime model validation in software using direct models, the approach employed here instead considered using indirect models of software execution, for example memory access patterns, to check for security intrusions. Additional research was performed to tackle the essential problem of model validation for systems which contain interactions with the physical world using hybrid automata models. Perpetual model validation will ensure that the actual behavior of the system conforms to the analysis model, raising the level of confidence that can be placed in the results due to formal nature of the analysis.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2017
Accession Number
AD1028363

Entities

People

  • Matthew L. Anderson
  • Stanley Bak
  • Steven Drager

Organizations

  • Rome Laboratory

Tags

Communities of Interest

  • Cyber
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Automata
  • Automation
  • Computer Science
  • Computer Security
  • Contracts
  • Control Systems
  • Cybersecurity
  • Detection
  • Embedded Systems
  • Engineering
  • Government Procurement
  • Governments
  • Hybrid Systems
  • Information Exchange
  • Information Operations
  • Information Processing
  • Information Systems
  • Intrusion
  • Nonlinear Dynamics
  • Nonlinear Systems
  • Robotics
  • Software Development
  • Systems Engineering
  • Test And Evaluation
  • United States

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computational Modeling and Simulation
  • Distributed Systems and Data Platform Development
  • Mathematical Modeling and Probability Theory.