Alexandre Dulaunoy

The paper investigates forecasting sparse and bursty vulnerability sightings, concluding that traditional time-series models like SARIMAX are inadequate, and count-based methods like Poisson regression offer more stable and interpretable alternatives.

The paper proposes GCVE, a decentralized, open, and extensible socio-technical model to standardize and enrich the entire lifecycle of vulnerability information, moving beyond simple identifier allocation.