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/ All Podcast Series / CSIAC Webinars / Cyber Security Game: A Model-based Game Theoretic Approach for Mitigating Cybersecurity Risk

CSIAC Webinars - Cyber Security Game: A Model-based Game Theoretic Approach for Mitigating Cybersecurity Risk

Posted: 09/17/2018 | Presenter: Scott Musman | 18 Comments

Notice: This podcast video may contain personal or third-party views and opinions not associated with the government.
Please see our terms of use located here: https://www.csiac.org/csiac-terms-of-use/
Recorded: 2018-09-20 | Series: CSIAC Webinars

The webinar video recording will be available shortly. Thank you for your patience.

This webinar describes the Cyber Security Game (CSG). CSG is a method that has been implemented in software that quantitatively identifies cyber security risks and uses this metric to determine the optimal employment of security methods for any given investment level. Cyber Security Game maximizes a system’s ability to operate in today’s contested cyber environment by minimizing its mission risk. The risk score is calculated by using a mission impact model to compute the consequences of cyber incidents and combining that with the likelihood that attacks will succeed. The likelihood of attacks succeeding is computed by applying a threat model to a system topology model and defender model. CSG takes into account the widespread interconnectedness of cyber systems, where defenders must defend all multi-step attack paths and an attacker only needs one to succeed. It employs a game theoretic solution using a game formulation that identifies defense strategies to minimize the maximum cyber risk (MiniMax). This webinar discusses the methods and models that compose Cyber Security Game. A limited example of a Point of Sale system is used to provide specific demonstrations of CSG models and analyses.

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Presenter

Scott Musman
Scott Musman
Scott Musman is a principal engineer at MITRE Corporation, a not-for-profit organization that operates research and development centers sponsored by the federal government. He holds a BSc(Hon) in Engineering from the University of Sussex in the U.K. and a MSc in Computer Science from Johns Hopkins University. In addition to working in other domains, such as image understanding, decision support, data-planning and reasoning under uncertainty, and data-mining, Scott has been working information security related problems since the mid 1990's, acting as director of R&D for Integrated Management Services Inc, head of the enterprise security research group for Alphatech/BAE Systems-AIT, and as a principal engineer for MITRE. Most recently he has been focusing on mission assurance by estimating and mitigating cyber related mission risks.

Tags: Active Defense, Cyber Threat Landscape, Game Theory, Risk Analysis

Previous in this Series:
« Learning to Win: Making the Case for...
Next in this Series:
Operational Technology Risk Assessment »

Reader Interactions

Ask a Question or Comment:

  1. CSIACAdmin

    2018-09-24 at 10:52

    This question was asked during the webinar:
    Is there supporting tool aids or models available in open source?

    Log in to Reply
  2. CSIACAdmin

    2018-09-24 at 10:54

    This question was asked during the webinar:
    Can Artificial Intelligence solutions be used to comprehensively reason about all of the possible cyber attacks?

    Log in to Reply
  3. CSIACAdmin

    2018-09-24 at 10:55

    This question was asked during the webinar:
    Are there supporting tool aids or models available in open source?

    Log in to Reply
  4. CSIACAdmin

    2018-09-24 at 11:00

    This comment was mentioned during the webinar:
    Boundaries of the Context (Model):
    More on Model Risk Management: Princeton Presentation: Future of Finance Beyond ‘Flash Boys’: Risk Modeling for Managing Uncertainty in an Increasingly Non-Deterministic Cyber World: Knight Reconsidered: Risk, Uncertainty, and Profit for the Cyber Era: https://ssrn.com/abstract=2590258 .

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    • CSIACAdmin

      2018-09-24 at 11:01

      This reply was given during the webinar:
      Known Bayesian priors and “rational” agents are underlying assumptions for adaptation strategies.

  5. CSIACAdmin

    2018-09-24 at 11:02

    This comment was given during the webinar:
    More on Model Risk Arbitrage (‘Black Hat’ Offensive Cybersecurity Approach): Princeton Presentation: Beyond Model Risk Management to Model Risk Arbitrage for FinTech Era: How to Navigate ‘Uncertainty’…When ‘Models’ Are ‘Wrong’…And Knowledge’…‘Imperfect’! Knight Reconsidered Again: Risk, Uncertainty, & Profit Beyond ZIRP & NIRP: https://ssrn.com/abstract=2766099

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  6. CSIACAdmin

    2018-09-24 at 11:02

    This comment was given during the webinar:
    More on Bayesian Modeling Markov Chain Monte Carlo Models in Cybersecurity: arkov Chain Monte Carlo Models, Gibbs Sampling, & Metropolis Algorithm for High-Dimensionality Complex Stochastic Problems: https://ssrn.com/abstract=2553537

    Log in to Reply
  7. CSIACAdmin

    2018-09-24 at 11:03

    This question was asked during the webinar:
    Does the model take into consideration the critical components within the system? If so, how are they addressed?

    Log in to Reply
  8. CSIACAdmin

    2018-09-24 at 11:03

    This comment was given during the webinar:
    On Limitations of Most Quant Models in Most Domains, particularly, Cyber Security Domains: Princeton Presentation: Model Risk Management in AI, Machine Learning & Deep Learning: https://ssrn.com/abstract=3167035

    Log in to Reply
  9. CSIACAdmin

    2018-09-24 at 11:04

    This comment was given during the webinar:
    Related Cyber Risk Insurance Expert Paper for NAIC (National Association of Insurance Commissioners): Advancing Cyber Risk Insurance Underwriting Model Risk Management beyond VaR to Pre-Empt and Prevent the Forthcoming Global Cyber Insurance Crisis: https://ssrn.com/abstract=3081492

    Log in to Reply
  10. CSIACAdmin

    2018-09-24 at 11:04

    This comment was given during the webinar:
    Armed Forces Communications and Electronics Association (AFCEA) C4I and Cyber Conference Paper: Synthesizing above Presentations on Models and Model Risks: AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning: https://ssrn.com/abstract=3193693

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  11. CSIACAdmin

    2018-09-24 at 11:05

    This comment was given during the webinar:
    Attack trees proven useful in highly constrained ICS/SCADA systems…

    Log in to Reply
  12. CSIACAdmin

    2018-09-24 at 11:06

    This question was asked during the webinar:
    Does your Models Research distinguish between ‘Uncertainty’ and ‘Risk’?

    Log in to Reply
  13. CSIACAdmin

    2018-09-24 at 11:06

    This question was asked during the webinar:
    Does your Models Research factor both ‘Uncertainty’ and ‘Risk’ in computation?

    Log in to Reply
  14. CSIACAdmin

    2018-09-24 at 11:06

    This question was asked during the webinar:
    Is the Model available for Public Use from MITRE?

    Log in to Reply
  15. CSIACAdmin

    2018-09-24 at 11:06

    This question was asked during the webinar:
    Where can one find more details about underlying research and papers (e.g. MITRE Web site)?

    Log in to Reply
  16. CSIACAdmin

    2018-09-24 at 11:07

    This question was asked during the webinar:
    What kind of information does one input into the model?

    Log in to Reply
  17. CSIACAdmin

    2018-09-24 at 11:08

    This question was asked during the webinar:
    So in essence it is necessary to have the information from the RMF to make this model work… right or wrong?

    Log in to Reply

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