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An empirical study of a vulnerability metric aggregation method
| Content Provider | CiteSeerX |
|---|---|
| Author | Homer, John Ou, Xinming Zhang, Su Singhal, Anoop |
| Abstract | Abstract—Quantifying security risk is an important and yet difficult task in enterprise network risk management, critical for proactive mission assurance. Even though metrics exist for individual vulnerabilities, there is currently no standard way of aggregating such metrics. We developed a quantitative model that can be used to aggregate vulnerability metrics in an enterprise network, with a sound computation model. Our model produces quantitative metrics that measure the likelihood that breaches can occur within a given network configuration, taking into consideration the effects of all possible interplays between vulnerabilities. In order to validate the effectiveness (scalability and accuracy) of this approach to realistic networks, we present the empirical study results of the approach on a number of system configurations. We use a real network as the test bed to demonstrate the utility of the approach, show that the sound computation model is crucial for interpreting the metric result. Keywords-enterprise network security; attack graph; vulnerability metrics, quantitative risk assessment I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Quantitative Model System Configuration Vulnerability Metric Aggregation Method Vulnerability Metric Sound Computation Model Standard Way Abstract Quantifying Security Risk Individual Vulnerability Attack Graph Empirical Study Result Test Bed Keywords-enterprise Network Security Proactive Mission Assurance Empirical Study Metric Result Enterprise Network Risk Management Quantitative Risk Assessment Enterprise Network Possible Interplay Quantitative Metric Realistic Network Real Network |
| Content Type | Text |
