Cyber-attack path discovery in a dynamic supply chain maritime risk management system

Maritime port infrastructures rely on the use of information systems for collaboration, while a vital part of collaborating is to provide protection to these systems. Attack graph analysis and risk assessment provide information that can be used to protect the assets of a network from cyber-attacks. Furthermore, attack graphs provide functionality that can be used to identify vulnerabilities in a network and how these can be exploited by potential attackers. Existing attack graph generation methods are inadequate in satisfying certain requirements necessary in a dynamic supply chain risk management environment, since they do not consider variables that assist in exploring specific network parts that satisfy certain criteria, such as the entry and target points, the propagation length and the location and capability of the potential attacker. In this paper, we present a cyber-attack path discovery method that is used as a component of a maritime risk management system. The method uses constraints and Depth-first search to effectively generate attack graphs that the administrator is interested in. To support our method and to show its effectiveness we have evaluated it using real data from a maritime supply chain

Assessing Security Risk to a Network Using a Statistical Model of Attacker Community Competence

We propose a novel approach for statistical risk modeling of network attacks that lets an operator perform risk analysis using a data model and an impact model on top of an attack graph in combination with a statistical model of the attacker community exploitation skill. The data model describes how data flows between nodes in the network -- how it is copied and processed by softwares and hosts -- while the impact model models how exploitation of vulnerabilities affects the data flows with respect to the confidentiality, integrity and availability of the data. In addition, by assigning a loss value to a compromised data set, we can estimate the cost of a successful attack. The statistical model lets us incorporate real-time monitor data from a honeypot in the risk calculation. The exploitation skill distribution is inferred by first classifying each vulnerability into a required exploitation skill-level category, then mapping each skill-level into a distribution over the required exploitation skill, and last applying Bayesian inference over the attack data. The final security risk is thereafter computed by marginalizing over the exploitation skill