Behavioral Drift Simulation Dataset for Insider Threat Detection in Aviation Cybersecurity

Black Swan failures in aviation cybersecurity represent significant and unpredictable disruptions that stem from intricate interactions among behavioral drift, flaws in system design, and organizational inertia, thereby eluding conventional, pattern-based threat detection methodologies. This study presents the Black Swan Cyber Resilience Framework (BSCRF), which integrates principles of antifragility and supraresilience into the realm of cybersecurity, emphasizing adaptive learning over static defensive measures. The BSCRF is structured around four interconnected layers: Signal, Inference, Decision, and Learning, each facilitating early detection of anomalies and dynamic recalibration of risk. A proof-of-concept simulation illustrates the framework's ability to identify subtle signals through trajectories of behavioral drift and to revise risk assessments via Bayesian inference. This methodology aids in closing the divide between behavioral anomaly detection and probabilistic threat modeling, thereby providing a proactive and adaptable cybersecurity strategy for complex aviation settings.