Skip to content

kind2-mc/VERDICT

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

VERDICT: Tools for architectural and behavioral analysis of AADL models

DARPA Cyber Assured Systems Engineering (CASE) Program

The goal of the DARPA CASE Program is to develop the necessary design, analysis and verification tools to allow system engineers to design-in cyber resiliency and manage tradeoffs as they do other nonfunctional properties when designing complex embedded computing systems. Cyber resiliency means the system is tolerant to cyberattacks in the same way that safety critical systems are tolerant to random faults—they recover and continue to execute their mission function. Achieving this goal requires research breakthroughs in:

  • the elicitation of cyber resiliency requirements before the system is built;

  • the design and verification of systems when requirements are not testable (i.e., when they are expressed in shall not statements);

  • tools to automatically adapt software to new non-functional requirements; and

  • techniques to scale and provide meaningful feedback from analysis tools that reside low in the development tool chain.

CASE Program Diagram

VERDICT Workflow

VERDICT is a framework to perform analysis of a system at the architectural level. It consists of two major functionalities: Model Based Architecture Analysis and Synthesis (MBAAS) and Cyber Resiliency Verification (CRV). VERDICT user starts with capturing an architectural model using AADL that represents the high-level functional components of the system along with the data flow between them; and then annotate the model with VERDICT properties, relations and requirements for analysis. The VERDICT MBAA back-end tool will analyze the architecture to identify cyber vulnerabilities and recommend defenses. User may also use the VERDICT MBAS feature to synthesize a minimal set of defenses with respect to their implementation costs. VERDICT also supports refinement of the architecture model with behavioral modeling information using AGREE. The VERDICT CRV back-end tool performs a formal analysis of the updated model with respect to formal cyber properties to identify vulnerabilities to cyber threat effects. This valuable capability provides an additional depth of analysis of a model that includes behavioral details of the architectural component models which will help to catch design mistakes earlier in the development process.

Publications

If you are citing VERDICT project, please use the following BibTex entries:

@Article{systems9010018,
AUTHOR = {Meng, Baoluo and Larraz, Daniel and Siu, Kit and Moitra, Abha and Interrante, John and Smith, William and Paul, Saswata and Prince, Daniel and Herencia-Zapana, Heber and Arif, M. Fareed and Yahyazadeh, Moosa and Tekken Valapil, Vidhya and Durling, Michael and Tinelli, Cesare and Chowdhury, Omar},
TITLE = {VERDICT: A Language and Framework for Engineering Cyber Resilient and Safe System},
JOURNAL = {Systems},
VOLUME = {9},
YEAR = {2021},
NUMBER = {1},
ARTICLE-NUMBER = {18},
URL = {https://www.mdpi.com/2079-8954/9/1/18},
ISSN = {2079-8954},
DOI = {10.3390/systems9010018}
}
@inproceedings{siu2019architectural,
  title={Architectural and behavioral analysis for cyber security},
  author={Siu, Kit and Moitra, Abha and Li, Meng and Durling, Michael and Herencia-Zapana, Heber and Interrante, John and Meng, Baoluo and Tinelli, Cesare and Chowdhury, Omar and Larraz, Daniel and others},
  booktitle={2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)},
  pages={1--10},
  year={2019},
  organization={IEEE}
}

For a complete list of publications related to VERDICT, please refer to the Wiki page .

Distribution Statement A: Approved for Public Release, Distribution Unlimited

Copyright © 2020, General Electric Company, Board of Trustees of the University of Iowa

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Java 85.1%
  • OCaml 13.7%
  • Xtend 0.8%
  • ANTLR 0.3%
  • Standard ML 0.1%
  • Dockerfile 0.0%