Infrastructure network protection under uncertain impacts of weaponized disinformation campaigns

The spread of false and misleading information through social networks is becoming increasingly prevalent, representing a low-cost way of potentially triggering a crisis with far-reaching consequences. While a substantial body of literature addresses direct attacks (i.e., cyberattacks such as viruses and ransomware), much less has been done to prepare for an emerging, over-the-horizon threat: adversaries who attack infrastructure indirectly by altering consumption behaviors of unwitting users influenced by weaponized disinformation. As a result, analyses of disinformation effects on critical infrastructure are limited, and data describing such attacks and their impacts are fraught with uncertainty. To address the uncertainty in the spread of and the response to disinformation, we propose a robust approach, integrating epidemiological and network optimization models to better understand and mitigate the effects of weaponized disinformation on infrastructure networks. This robust formulation offers a more stable and effective method to address uncertainty during disinformation campaigns that can pose a significant risk to infrastructure networks. To demonstrate the applicability and efficacy of the proposed model, we present a case study focused on the electric power grid in Los Angeles County. This example highlights the importance of developing robust and effective solutions to address the challenges posed by disinformation campaigns, particularly in critical infrastructure networks. Furthermore, this example emphasizes the need for a more proactive approach to safeguard infrastructure networks against such threats.