AI & Game Theory for Cybersecurity
Can game theory help identify a rational balance between cybersecurity investment and residual attack risk?
Why I Started This Project
I became interested in the concept of Nash equilibrium after watching the movie A Beautiful Mind. It's a game theory idea that a system can reach a state where no participant benefits by changing strategy independently.
That led me to think about cybersecurity. An attacker and a defender are making competing strategic decisions, but defensive resources are finite. Increasing security can reduce risk, but additional protection also carries cost, and eliminating all risk may be impractical.
I began wondering whether this interaction could be modeled as a game: Is there an equilibrium at which a defender's security investment represents a rational response to an attacker's strategy, and beyond which additional defensive investment produces diminishing strategic benefit?
This project grew out of that question. My goal is not to construct arbitrary game payoffs that produce an equilibrium, but to investigate how attacker and defender incentives can be modeled using defensible assumptions and then determine what game-theoretic and AI methods can tell us about the resulting security decisions.
Research Question
Can an attacker-defender cybersecurity interaction be represented with a mathematically defensible game model, and can equilibrium analysis help identify a rational level of defensive investment relative to the remaining attack risk?
Current Approach
The project is currently focused on defining the game itself: identifying the attacker and defender strategies, determining which costs and benefits belong in their utility functions, and establishing how those values can be justified rather than chosen simply to produce a desired result.
Once the model is defensible, I plan to examine its equilibrium behavior and explore how AI techniques can be used to analyze or learn strategies within more complex versions of the attacker-defender environment.
Current Status
Research and model development are in progress. The current focus is on the mathematical formulation and assumptions behind the attacker-defender game before moving into implementation and experimentation.
Next Steps
- Define attacker and defender strategy spaces.
- Develop and justify payoff and utility functions.
- Analyze the initial model for Nash equilibria.
- Implement the model and validate its behavior experimentally.
- Explore AI-based strategy learning as the model becomes more complex.