Situational awareness and information collection from critical infrastructure

Critical infrastructure (CI) is a complex part of society consisting of multiple sectors. Although these sectors are usually administered independently, they are functionally interconnected and interdependent. This paper presents a concept and a system that is able to provide the common operating picture (COP) of critical infrastructure (CI). The goal is to provide support for decision making on different management layers. The developed Situational Awareness of Critical Infrastructure and Networks (SACIN) framework implements key features of the system and is used to evaluate the concept. The architecture for the SACIN framework combines an agent-based brokered architecture and Joint Directors of Laboratories (JDL) data fusion model. In the SACIN context, agent software produces events from the source systems and is maintained by the source system expert. The expert plays an important role, as he or she is the specialist in understanding the source system. He or she determines the meaningful events from the system with provided guidelines. The brokered architecture provides scalable platform to allow a large number of software agents and multiple analysis components to collaborate, in accordance with the JDL model. A modular and scalable user interface is provided through a web application and is usable for all SACIN participants. One of the main incentives for actors to provide data to the SACIN is the resultant access to the created COP.The proposed concept provides improved situational awareness by modeling the complex dependency network within CI. The current state of the infrastructure can be determined by combining and analyzing event streams. Future states can be proactively determined by modeling dependencies between actors. Additionally, it is possible to evaluate the impact of an event by simulating different scenarios according to real-world and hypothetical use cases. As a result, understanding of CI and the ability to react to anomalies is improved am- ngst the decision makers.