Security Optimization of Dynamic Networks with Probabilistic Graph Modeling and Linear Programming - 2015
Securing the networks of huge organizations is technically challenging because of the complex configurations and constraints. Managing these networks requires rigorous and comprehensive analysis tools. A network administrator wants to identify vulnerable configurations, along with tools for hardening the networks. Such networks usually have dynamic and fluidic structures, so one may have incomplete info regarding the connectivity and availability of hosts. In this paper, we have a tendency to address the problem of statically performing a rigorous assessment of a group of network security defense ways with the goal of reducing the probability of a successful massive-scale attack in a very dynamically changing and complicated network design. We describe a probabilistic graph model and algorithms for analyzing the safety of advanced networks with the ultimate goal of reducing the likelihood of successful attacks. Our model naturally utilizes a scalable state-of-the-art optimization technique known as sequential linear programming that is extensively applied and studied in varied engineering problems. In comparison to connected solutions on attack graphs, our probabilistic model provides mechanisms for expressing uncertainties in network configurations, that is not reported elsewhere. We have performed comprehensive experimental validation with real-world network configuration data of a sizable organization.
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