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handle: 10454/17916
Year in and year out the increasing adaptivity of offenders has maintained ransomware's position as a major cybersecurity threat. The cybersecurity industry has responded with a similar degree of adaptiveness, but has focussed more upon technical (science) than ‘non-technical’ (social science) factors. This article explores empirically how organisations and investigators have reacted to the shift in the ransomware landscape from scareware and locker attacks to the almost exclusive use of crypto-ransomware. We outline how, for various reasons, victims and investigators struggle to respond effectively to this form of threat. By drawing upon in-depth interviews with victims and law enforcement officers involved in twenty-six crypto-ransomware attacks between 2014 and 2018 and using an inductive content analysis method, we develop a data-driven taxonomy of crypto-ransomware countermeasures. The findings of the research indicate that responses to crypto-ransomware are made more complex by the nuanced relationship between the technical (malware which encrypts) and the human (social engineering which still instigates most infections) aspects of an attack. As a consequence, there is no simple technological ‘silver bullet’ that will wipe out the crypto-ransomware threat. Rather, a multi-layered approach is needed which consists of socio-technical measures, zealous front-line managers and active support from senior management.
Crypto-ransomware, Cybercrime, Management support, Social engineering, Security countermeasures, Malware, 004, Organisational settings
Crypto-ransomware, Cybercrime, Management support, Social engineering, Security countermeasures, Malware, 004, Organisational settings
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 87 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |