Difference between revisions of "Metrics for Mitigating Cybersecurity Threats to Networks"

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==Key Words==  
 
==Key Words==  
[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]
+
[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==

Latest revision as of 15:05, 17 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

BibTeX

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.

Additional Notes and Highlights