Quantifying Illegal Activity: Estimating Dark Rates and Predicting Offenses
At a glance
- Project leader : Dr. Andrea Maria Günster
- Deputy of project leader : Nicole Bellert, Maria Pelli
- Project team : Raphael Arnold, Prof. Damian Kozbur
- Project budget : CHF 905'238
- Project status : ongoing
- Funding partner : Innosuisse (Innovationsprojekt / Projekt Nr. 102.134.1 IP-ICT)
- Project partner : LogObject AG, Universität Zürich
- Contact person : Andrea Maria Günster
Innosuisse supports our research "Quantifying Illegal Activity: Estimating Dark Rates and Predicting Offenses in Switzerland." In collaboration with LogObject AG and the University of Zurich (UZH), we will estimate the number of undetected cyber-attacks and predict high-risk areas for burglaries using real life data for Switzerland. To address the research question "How can we quantify undetected illegal activity?", we develop new statistical methods, implement machine learning algorithms, and apply graph-based models.
In this partnership with LogObject and the UZH, we look forward to consolidating and transforming our findings into useful applications for Swiss police services. Our work aims to support law enforcement, by more accurately predicting burglaries and the dark rate of cyber-attacks, thereby enabling prevention in Switzerland.