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Title: Determining the Impact of Cybersecurity Failures During and Attributable to Pandemics and Other Emergency Situations
In emergency situations, such as the current COVID-19 pandemic, less immediate concerns such as cybersecurity and long-term economic impact can fall by the wayside. This paper presents a discussion of the impact of cybersecurity issues that occur during and are attributable to pandemics and other emergency situations. This discussion is facilitated by a simulation tool, the Disaster Vulnerability Threat and Impact Simulator System (DVTISS). DVTISS simulates the network structure, security measures, user characteristics and demographics, data, and devices of an organization or region’s computing infrastructure. The system is provided input parameters and performs analysis to identify the combined results of numerous different decisions, which are made in concert, to identify the types of vulnerabilities that may be present and the impact of their exploitation. The impacts of system unavailability are considered. This can aid businesses, governments and others in determining the level of prioritization that should be given to cybersecurity considerations. The simulator can also be used for disaster preparedness and planning, evaluating particular response strategies and the evaluation of laws and policies that impact IT decision making during emergencies. This paper uses the DVTISS tool to consider organizational responses to several example emergency situations. It demonstrates the utility of the tool as well as its efficacy for decision making support. Based on the example emergencies, the paper also discusses key areas of vulnerability during emergency situations and their financial, data and system outage impacts. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  more » « less
Award ID(s):
1757659
NSF-PAR ID:
10223347
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
IEEE Applied Imagery Pattern Recognition Workshop
ISSN:
2164-2516
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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