Temporal assessment of malicious behaviors: application to turnout field data monitoring
This addresses cybersecurity risks in railway infrastructure monitoring, but appears incremental as it applies existing prediction-comparison techniques to a specific domain.
The paper tackled the problem of detecting cyberattacks on monitored railway turnout data by proposing a method that compares predictions from temporal behavior evolution with field data to identify discrepancies, and demonstrated it on real-life data.
Monitored data collected from railway turnouts are vulnerable to cyberattacks: attackers may either conceal failures or trigger unnecessary maintenance actions. To address this issue, a cyberattack investigation method is proposed based on predictions made from the temporal evolution of the turnout behavior. These predictions are then compared to the field acquired data to detect any discrepancy. This method is illustrated on a collection of real-life data.