Objectives: Aiming to reduce distributed denial-of-service (DDoS) attacks by alerting the consciences of Internet users, this paper evaluates the effectiveness of four warning banners displayed as online ads (deterrent—control, social, informative, and reorienting) and the contents of their two linked landing pages. Methods: We implement a 4 x 2 quasi-experimental design on a self-selected sample of Internet users to measure the engagement generated by the ads and the pages. Engagement is measured on the ads as the ratio of clicks to impressions, and on the pages as percentage of page scrolled, average session duration, video interaction rate, and URLs click rate. Results: Social ads generate significantly more engagement than the rest with low to medium effect sizes. Data reveal no differences in engagement between both landing page designs. Conclusions: Social messages may be a better alternative for engaging with potential cyber offenders than the traditional deterrent messages. Correspondence: Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), De Boelelaan 1077, 1081 HV, Amsterdam, The Netherlands. Email: AMoneva@nscr.n This is a post-peer-review, pre-copyedit version of an article published in Journal of Experimental Criminology. The final authenticated version is available online at: https://link.springer.com/article/10.1007/s11292-022-09504-2
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Crime script analysis as a methodology to analyse criminal processes is underdeveloped. This is apparent from the various approaches in which scholars apply crime scripting and present their cybercrime scripts. The plethora of scripting methods raise significant concerns about the reliability and validity of these scripting studies. In this methodological paper, we demonstrate how object-oriented modelling (OOM) could address some of the currently identified methodological issues, thereby refining crime script analysis. More specifically, we suggest to visualise crime scripts using static and dynamic modelling with the Unified Modelling Language (UML) to harmonise cybercrime scripts without compromising their depth. Static models visualise objects in a system or process, their attributes and their relationships. Dynamic models visualise actions and interactions during a process. Creating these models in addition to the typical textual narrative could aid analysts to more systematically consider, organise and relate key aspects of crime scripts. In turn, this approach might, amongst others, facilitate alternative ways of identifying intervention measures, theorising about offender decision-making, and an improved shared understanding of the crime phenomenon analysed. We illustrate the application of these models with a phishing script.
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The Annual Conference on the Human Factor in Cybercrime is a small and specialised scientific event that aims to bring together scholars from around the world to present their research advances to a select audience. Its dynamic and linear format favours group discussions since all contributions are heard by all the attendants. This, together with its tailored social scheme, promotes interaction between members, which—in turn—leads to new collaborations. However, it has not yet been analysed whether the design of the conference actually encourages varied participation and fosters collaborative networks among its participants. The purpose of this chapter is to assess participation in the 2018 and 2019 editions to determine whether this is the case. Using descriptive analyses, here we show how participation in the conference has varied and examine the composition of the collaboration networks among the participants. The results show an increased and more diverse participation in the 2019 meeting along with a greater presence of stakeholders. Furthermore, the findings reveal that members of previously established organisations play an important role in cohering the network. Yet few connections exist between academia and practice. A further analysis of the strengths and weaknesses identified in the two editions of the conference serves to elaborate a series of recommendations for future editions.
