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Quantifying Illegal Activity: Estimating Dark Rates and Predicting Offenses

Beschreibung

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.

Eckdaten

Projektleitung

Stellv. Projektleitung

Projektteam

Raphael Arnold, Prof. Damian Kozbur, Eduardas Lazebnyj, Luca Persia

Projektpartner

LogObject AG; Universität Zürich

Projektstatus

laufend, gestartet 11/2022

Institut/Zentrum

Institut für Wealth and Asset Management (IWA); Institut für Marketing Management (IMM); Institut für Wirtschaftsinformatik (IWI); Fachstelle für Wirtschaftspolitik (FWP)

Drittmittelgeber

Innovationsprojekt / Projekt Nr. 102.134.1 IP-ICT

Projektvolumen

905'238 CHF