Harvesting the low-hanging fruits: defending against automated large-scale cyber-intrusions by focusing on the vulnerable population

摘要: The orthodox paradigm to defend against automated social-engineering attacks in large-scale socio-technical systems is reactive and victim-agnostic. Defenses generally focus on identifying the attacks/attackers (e.g., phishing emails, social-bot infiltrations, malware offered for download). To change status quo, we propose identify, even if imperfectly, vulnerable user population, that is, users are likely fall victim such attacks. Once identified, information about population can be used two ways. First, influenced by defender through several means including: education, specialized experience, extra protection layers watchdogs. In same vein, ultimately fine-tune reprioritize defense mechanisms offer differentiated protection, possibly at cost of additional friction generated mechanism. Secondly, identify an attack (or compromised users) based differences between general population. This paper considers implications proposed existing defenses three areas (phishing credentials, distribution socialbot infiltration) discusses how using knowledge enable more robust defenses.