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<p>The current generation of network vulnerability detection software uses databases of known vulnerabilities and scans  target networks for these weaknesses. The results can be voluminous and difficult to assess. Thus, the success of this technology  has created a need for software to aid in network vulnerability analysis. Although research has shown the effectiveness of  automated attack graph generation tools in displaying potential attack paths in a network, research involving the performance of  these tools has been limited. Using empirical testing, we have collected quantitative data using CAULDRON, an attack graph  generation tool developed at George Mason University, on a collection of simulated networks. By defining our model to include  sets of nodes, which allow connectivity from all nodes to all nodes in the set; the number of nodes present in each set, the number  of connections between sets; and the number of vulnerabilities per node as our variables, we are able to observe the performance  impact on CAULDRON of connectivity and the increased presence of vulnerabilities in our networks. The effect of these variables  on processing time and memory usage is presented and can be used as a metric to assess the scalability of this tool within various  customer environments.
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<br>Subjects: Computer science; Performance; Software engineering; Computers</div>
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Cullum, James J.</div>
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