Metrics for Mitigating Cybersecurity Threats to Networks: Difference between revisions
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==Key Words== | ==Key Words== | ||
[http://cyber.law.harvard.edu/cybersecurity/Glossary_of_Core_Ideas#Risk_Aversion risk | [http://cyber.law.harvard.edu/cybersecurity/Glossary_of_Core_Ideas#Risk_Aversion risk aversion], [http://cyber.law.harvard.edu/cybersecurity/Glossary_of_Core_Ideas#Risk_Modeling risk modeling] [http://cyber.law.harvard.edu/cybersecurity/Glossary_of_Core_Ideas#Network_Security network security] | ||
==Synopsis== | ==Synopsis== | ||
Revision as of 12:21, 9 June 2010
Full Title of Reference
Metrics for Mitigating Cybersecurity Threats to Networks
Full Citation
Norman Schneidewind, Metrics for Mitigating Cybersecurity Threats to Networks, 14 IEEE Internet Computing 1 (2010). Purchase
Categorization
Issues: Metrics
Key Words
risk aversion, risk modeling network security
Synopsis
To achieve their full potential, networks must be secure as well as functional. With this in mind, the author identifies metrics designed to mitigate vulnerabilities to cyberattacks in networks that are key to the critical infrastructure of the US. He discusses both growth metrics — based on data obtained from the US National Institute of Standards and Technology and Department of Homeland Security vulnerability database — and metrics designed to mitigate the risk of security vulnerabilities in networks. If used together, these two types of metrics can help make networks more secure.