Efficient Minimum-Cost Network Hardening Via Exploit Dependency Graphs
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
Risk Management using Behavior based Attack Graphs
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Managing attack graph complexity through visual hierarchical aggregation
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
A scalable approach to attack graph generation
Proceedings of the 13th ACM conference on Computer and communications security
Practical Attack Graph Generation for Network Defense
ACSAC '06 Proceedings of the 22nd Annual Computer Security Applications Conference
Toward measuring network security using attack graphs
Proceedings of the 2007 ACM workshop on Quality of protection
Attack graph based evaluation of network security
CMS'06 Proceedings of the 10th IFIP TC-6 TC-11 international conference on Communications and Multimedia Security
SP 800-30. Risk Management Guide for Information Technology Systems
SP 800-30. Risk Management Guide for Information Technology Systems
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The growth in the size of networks and the number of vulnerabilities is increasingly challenging to manage network security. Especially, difficult to manage are multi-step attacks which are attacks using one or more vulnerabilities as stepping stones. Attack graphs are widely used for analyzing multistep attacks. However, since these graphs had large sizes, it was too expensive to work with. In this paper, we propose a mechanism to manage attack graphs using a divide and conquer approach. To enhance efficiency of risk analyzer working with attack graphs, we converted a large graph to multiple sub-graphs named risk units and provide the light-weighted graphs to the analyzers. As a result, when k order of time complexity algorithms work with an attack graph with n vertices, a division having c of overhead vertices reduces the workloads from nk to r(n + c)k. And the coefficient r becomes smaller geometrically from 2-k depended on their division rounds. By this workload reduction, risk assessment processes which work with large size attack graphs become more scalable and resource practical.