A Queueing-Theoretic Framework for Dynamic Attack Surfaces: Data-Integrated Risk Analysis and Adaptive Defense

This project is an academic contribution (linked to an ArXiv paper) rather than a software product. With 0 stars and only 5 days of age, it lacks any market traction or software-based moat. Its defensibility is low because it is a theoretical model that can be easily replicated or refined by other researchers. However, the 'AI amplification factor' approach to modeling the race between automated attackers and defenders is a novel combination of queueing theory and contemporary cybersecurity trends. Frontier labs are unlikely to compete here as they focus on building the generative capabilities (agents that find/fix bugs) rather than the macro-statistical modeling of those phenomena. The primary value lies in its potential use by CISOs or policy makers for strategic planning. Competitors would include risk-modeling incumbents like Kenna Security (Cisco) or Tenable, though they use empirical data-driven models rather than queueing-theoretic ones. The 4 forks on a 0-star repo suggest early-stage academic interest or automated indexing rather than developer adoption.