No abstract

No abstract

No abstract

Abstract-Attack graph analysis has been established as a powerful tool for analyzing network vulnerability. However, previous approaches to network hardening look for exact solutions and thus do not scale. Further, hardening elements have been treated independently, which is inappropriate for real environments. For example, the cost for patching many systems may be nearly the same as for patching a single one. Or patching a vulnerability may have the same effect as blocking traffic with a firewall, while blocking a port may deny legitimate service. By failing to account for such hardening interdependencies, the resulting recommendations can be unrealistic and far from optimal. Instead, we formalize the notion of hardening strategy in terms of allowable actions, and define a cost model that takes into account the impact of interdependent hardening actions. We also introduce a nearoptimal approximation algorithm that scales linearly with the size of the graphs, which we validate experimentally.

Attack graphs have been widely used for attack modeling, alert correlation, and prediction. In order to address the limitations of current approaches -scalability and impact analysis -we propose a novel framework to analyze massive amounts of alerts in real time, and measure the impact of current and future attacks. Our contribution is threefold. First, we introduce the notion of generalized dependency graph, which captures how network components depend on each other, and how the services offered by an enterprise depend on the underlying infrastructure. Second, we extend the classical definition of attack graph with the notion of timespan distribution, which encodes probabilistic knowledge of the attacker's behavior. Finally, we introduce attack scenario graphs, which combine dependency and attack graphs, bridging the gap between known vulnerabilities and the services that could be ultimately affected by the corresponding exploits. We propose efficient algorithms for both detection and prediction, and show that they scale well for large graphs and large volumes of alerts. We show that, in practice, our approach can provide security analysts with actionable intelligence about the current cyber situation, enabling them to make more informed decisions.