TAON: an ontology-based approach to mitigating targeted attacks

Targeted attacks on IT systems are a rising threat against the confidentiality of sensitive data and the availability of systems and infrastructures. Planning for the eventuality of a data breach or sabotage attack has become an increasingly difficult task with the emergence of advanced persistent threats (APTs), a class of highly sophisticated cyber-attacks that are nigh impossible to detect using conventional signature-based systems. Understanding, interpreting, and correlating the particulars of such advanced targeted attacks is a major research challenge that needs to be tackled before behavior-based approaches can evolve from their current state to truly semantics-aware solutions. Ontologies offer a versatile foundation well suited for depicting the complex connections between such behavioral data and the diverse technical and organizational properties of an IT system. In order to facilitate the development of novel behavior-based detection systems, we present TAON, an OWL-based ontology offering a holistic view on actors, assets, and threat details, which are mapped to individual abstracted events and anomalies that can be detected by today's monitoring data providers. TOAN offers a straightforward means to plan an organization's defense against APTs and helps to understand how, why, and by whom certain resources are targeted. Populated by concrete data, the proposed ontology becomes a smart correlation framework able to combine several data sources into a semantic assessment of any targeted attack.