Accelerating Threat and Risk Detection within Data Centric Networks

Continuous monitoring of critical distributed systems is essential for the early detection of risks and security threats. However, traditional networks are opaque and do not provide the transparency needed for advanced data monitoring. Existing monitoring tools can identify network-layer information but cannot observe data semantics. Next-generation data-centric networks present an opportunity to revolutionize network monitoring. In data-centric networks, data semantics are decoupled from the specifics of the application stack, so a trusted observer can not only identify data flows but also understand the semantics of the communications. Monitoring and visualizing data streams and their semantics can accelerate threat detection by identifying undesired communication flows caused by misconfigurations, bugs, or an intruder, and answer questions such as who is talking to who, and what are they talking about? RTI’s product is the world’s leading implementation of the OMG DDS standard and military-grade data-centric connectivity software. We propose to design, implement and test a revolutionary monitoring framework for Air Force’s next-gen data-centric networks. If fully funded, the framework will accelerate threat detection by visualizing data streams and extending AI/ML-based monitoring and data analytic tools. The framework will also enable automatic system validation against UAF models and other security standards. In Phase I, we will collect stakeholders' requirements and derisk Phase II.