Tag Archives: attack graphs

Efficient Attack Graph Analysis through Approximate Inference

Attack graphs provide compact representations of the attack paths an attacker can follow to compromise network resources from the analysis of network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk of compromise to the system’s 
components given their vulnerabilities and interconnections and accounts for multi-step attacks spreading through the system. While static analysis considers the risk posture at rest, dynamic analysis also accounts for evidence of compromise, for example, from Security Information and Event Management software or forensic investigation. However, in this context, exact Bayesian inference techniques do not scale well. In this article, we show how Loopy Belief Propagation—an approximate inference technique—can be applied to attack graphs and that it scales linearly in the number of nodes for both static and dynamic analysis, making such analyses viable for larger networks. We experiment with different topologies and network clustering on synthetic Bayesian attack graphs with thousands of nodes to show that the algorithm’s accuracy is acceptable and that it converges to a stable solution. We compare sequential and parallel versions of Loopy Belief Propagation with exact inference techniques for both static and dynamic analysis, showing the advantages and gains of approximate inference techniques when scaling to larger attack graphs.

Luis Muñoz-González, Daniele Sgandurra, Andrea Paudice, Emil C. Lupu. “Efficient Attack Graph Analysis through Approximate Inference.” ACM Transactions on Privacy and Security, vol. 20(3), pp. 1-30, 2017.

Exact Inference Techniques for the Analysis of Bayesian Attack Graphs

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise network resources. The uncertainty about the attacker’s behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.

Luis Muñoz-González, Daniele Sgandurra, Martín Barrere, and Emil C. Lupu. “Exact Inference Techniques for the Analysis of Bayesian Attack Graphs.” IEEE Transactions on Dependable and Secure Computing (in press), 2017.

Exact Inference Techniques for the Dynamic Analysis of Attack Graphs

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise valuable network resources. The uncertainty about the attacker’s behaviour and capabilities make Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of traditional attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our proposed approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.

Luis Muñoz-González, Daniele Sgandurra, Martín Barrere, and Emil C. Lupu: Exact Inference Techniques for the Dynamic Analysis of Attack Graphs. arXiv preprint: arXiv:1510.02427. October, 2015.

CIPART: Cloud Intelligent Protection at Run-Time

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Organisations, small and large, increasingly rely upon cloud environments to supply their ICT needs because clouds provide a better incremental cost structure, resource elasticity and simpler management. This trend is set to continue as increasingly information collected from mobile devices and smart environments including homes, infrastructures and smart-cities is uploaded and processed in cloud environments. Services delivered to users are also deployed in the cloud as this provides better scaleability and in some cases permits migration closer to the point of access for reduced latency.

Clouds are therefore an attractive target for organised and skilled cyber-attacks. They are also more vulnerable as they host environments from multiple tenant organisations with different interests and different risk aversion profiles. Yet clouds also offer opportunities for better protection both pro-actively and reactively in response to a persistent attack. Continue reading