Knowledge Engineering for Cybersituational Awareness and Cyberthreat Intelligence

Proactive network monitoring, vulnerability assessment, and traffic path estimation rely on demanding processes that require expert knowledge and are time consuming, owing to the complexity of network topologies and network traffic flow. Therefore, the automated processing of network data is very much desired, however, constructing a machine-interpretable representation of network topologies and traffic flow is not trivial due to interoperability, complexity, and scalability issues. Many of these issues can be addressed by Semantic Web standards, such as RDF and OWL, which enable automated tasks to determine whether the traffic goes through a particular country, empower organizations to develop proactive cybersecurity policies, and inform decision makers in a timely manner.

Dr. Sikos employs formal knowledge representation and ontology engineering in this field in the Knowledge and Software Engineering Lab at the University of South Australia, and works closely with the DST Group and collaborates with Data61 and the Knowledge Discovery and Management Research Group of the University of Sydney.

As a knowledge engineer, Dr. Sikos previously became internationally recognized for his results in the knowledge representation of multimedia resources, in particular in content-based video retrieval and ontology-based video scene interpretation via spatiotemporal reasoning, and for his MPEG-7 and X3D standardization efforts.