Print Email Facebook Twitter Who is next? Title Who is next?: Identifying characteristics of European banks that are key in influencing the target selection of banking malware. Author Hoppenreijs, Marrit (TU Delft Technology, Policy and Management) Contributor van Eeten, Michel (mentor) Hernandez Ganan, Carlos (mentor) Warnier, Martijn (graduation committee) Perk, Diederik (graduation committee) Degree granting institution Delft University of Technology Programme Engineering and Policy Analysis Date 2019-03-21 Abstract The European financial sector is a frequent victim of banking malware leading to massive losses. It appears that not all customers’ banks are equally attractive targets among cybercriminals who deploy banking malware. This research established a comprehensive regression model explaining why certain banks are more attractive to cybercriminals. The model proves that large banks, banks that are part of a banking group, banks with a high brand value, and banks which websites have a high domain-popularity, bear a higher probability to be (more frequently) targeted. Two-factor authentication doesn’t seem as effective as might expected. The use of this security measure does not decrease the chance for a bank to be targeted. However, the presence of this measure has an influence on a lower target frequency. Furthermore, it is shown that banks offering a largely spoken language on their website do not ease the banking malware attacks. Further research is needed to enhance and improve the model. The independence in the model can be reduced and more bank characteristics could be added, especially factors related to the ease to launder money. Subject banking malwaretarget selectiononline bankingcybercrimeQuantitative analysisbank-size metric To reference this document use: http://resolver.tudelft.nl/uuid:1cb5bb8d-eae4-4acb-a28d-d61ec9897d34 Part of collection Student theses Document type master thesis Rights © 2019 Marrit Hoppenreijs Files PDF Master_Thesis_Marrit_Hopp ... nreijs.pdf 4.05 MB Close viewer /islandora/object/uuid:1cb5bb8d-eae4-4acb-a28d-d61ec9897d34/datastream/OBJ/view