Búsqueda

Modelling maximum cyber incident losses of German organisations

Portada
Registro MARC
Tag12Valor
LDR  00000cab a2200000 4500
001  MAP20230012390
003  MAP
005  20231214132325.0
008  230612e20230403esp|||p |0|||b|spa d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎861
1001 ‎$0‎MAPA20230005224‎$a‎Skarczinski, Bennet von
24500‎$a‎Modelling maximum cyber incident losses of German organisations‎$c‎Bennet von Skarczinski, Mathias Raschke, Frank Teuteberg
520  ‎$a‎In this paper, we introduce a novel tempered' generalised extreme value (GEV) approach. Based on a stratified random sample of 5000 interviewed German organisations, we model different loss distributions and compare them to our empirical data using graphical analysis and goodness-of-fit tests. We differentiate various subsamples (industry, size, attack type, loss type) and find our modified GEV outperforms other distributions, such as the lognormal and Weibull distributions. Finally, we calculate losses for the German economy, present application examples, derive implications as well as discuss the comparison of loss estimates in the literature
650 4‎$0‎MAPA20140022700‎$a‎Ciberseguridad
650 4‎$0‎MAPA20080570651‎$a‎Siniestralidad
650 4‎$0‎MAPA20080618919‎$a‎Análisis de siniestralidad
650 4‎$0‎MAPA20080548575‎$a‎Pérdidas
650 4‎$0‎MAPA20080622251‎$a‎Seguridad de la información
650 4‎$0‎MAPA20210003882‎$a‎Secuestro de datos
7001 ‎$0‎MAPA20230005231‎$a‎Raschke, Mathias
7001 ‎$0‎MAPA20230005248‎$a‎Teuteberg, Frank
7730 ‎$w‎MAP20077100215‎$g‎03/04/2023 Volumen 48 Número 2 - abril 2023 , p. 463-501‎$x‎1018-5895‎$t‎Geneva papers on risk and insurance : issues and practice‎$d‎Geneva : The Geneva Association, 1976-
85600‎$y‎MÁS INFORMACIÓN‎$u‎ mailto:centrodocumentacion@fundacionmapfre.org?subject=Consulta%20de%20una%20publicaci%C3%B3n%20&body=Necesito%20m%C3%A1s%20informaci%C3%B3n%20sobre%20este%20documento%3A%20%0A%0A%5Banote%20aqu%C3%AD%20el%20titulo%20completo%20del%20documento%20del%20que%20desea%20informaci%C3%B3n%20y%20nos%20pondremos%20en%20contacto%20con%20usted%5D%20%0A%0AGracias%20%0A