Attack Modeling and Mitigation Strategies for Risk-Based Analysis of Networked Medical Devices

Simple item page
dc.contributor.authorHodges, Bronwyn
dc.contributor.authorMcdonald, Jeffrey
dc.contributor.authorGlisson, William
dc.contributor.authorJacobs, Mike
dc.contributor.authorVan Devender, Maureen
dc.contributor.authorPardue, Harold
dc.date.accessioned2020-01-04T08:32:06Z
dc.date.available2020-01-04T08:32:06Z
dc.date.issued2020-01-07
dc.description.abstractThe escalating integration of network-enabled medical devices raises concerns for both practitioners and academics in terms of introducing new vulnerabilities and attack vectors. This prompts the idea that combining medical device data, security vulnerability enumerations, and attack-modeling data into a single database could enable security analysts to proactively identify potential security weaknesses in medical devices and formulate appropriate mitigation and remediation plans. This study introduces a novel extension to a relational database risk assessment framework by using the open-source tool OVAL to capture device states and compare them to security advisories that warn of threats and vulnerabilities, and where threats and vulnerabilities exist provide mitigation recommendations. The contribution of this research is a proof of concept evaluation that demonstrates the integration of OVAL and CAPEC attack patterns for analysis using a database-driven risk assessment framework.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.796
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64538
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning and Cyber Threat Intelligence and Analytics
dc.subjectcyber threat
dc.subjectmedical devices
dc.subjectrisk analysis
dc.subjectthreat intelligence
dc.subjectvulnerabilities
dc.titleAttack Modeling and Mitigation Strategies for Risk-Based Analysis of Networked Medical Devices
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0643.pdf
Size:
2.36 MB
Format:
Adobe Portable Document Format
Download

Collections

Machine Learning and Cyber Threat Intelligence and Analytics