Identifying interdependencies using attack graph generation methods

Information and communication technologies have augmented interoperability and rapidly advanced varying industries, with vast complex interconnected networks being formed in areas such as safety-critical systems, which can be further categorised as critical infrastructures. What also must be considered is the paradigm of the Internet of Things which is rapidly gaining prevalence within the field of wireless communications, being incorporated into areas such as e-health and automation for industrial manufacturing. As critical infrastructures and the Internet of Things begin to integrate into much wider networks, their reliance upon communication assets by third parties to ensure collaboration and control of their systems will significantly increase, along with system complexity and the requirement for improved security metrics. We present a critical analysis of the risk assessment methods developed for generating attack graphs. The failings of these existing schemas include the inability to accurately identify the relationships and interdependencies between the risks and the reduction of attack graph size and generation complexity. Many existing methods also fail due to the heavy reliance upon the input, identification of vulnerabilities, and analysis of results by human intervention. Conveying our work, we outline our approach to modelling interdependencies within large heterogeneous collaborative infrastructures, proposing a distributed schema which utilises network modelling and attack graph generation methods, to provide a means for vulnerabilities, exploits and conditions to be represented within a unified model.