Machine Learning for Secure and Resilient Information Management in Combat Cloud

We propose to learn the mission interests dynamically, and optimally store and forward the information generated by the nodes as the mission evolves to C2 using Reinforcement Learning (RL). In this forwarding process, we will focus on identifying the trending mission interests (related to updated missions or events) for continuous decision-making by considering the mobility and connectivity of the nodes and considering the changes of the perceived network model and accordingly modify appropriate ‘reward’ functions based on past learning. The machine learning will help in learn-and-adapt to space-time evolution of data requests as well as local policies used by at nodes in determining the currently cached objects and what should be prefetched next along with determining how many objects need to be prefetched based on determined mission priorities, expected latency, etc. These features, in practice, usually exhibit unknown and temporal dynamics because the most popular content at the current epoch may not receive the highest attention in the future; and mobile users could change locations as time passes. The combat cloud in contested environments faces challenges while making the prioritized data available to different groups in a timely and secure fashion (authenticated, un-tempered, and trusted). For secure, end-to-end, mission-oriented, data dissemination, it needs a resilient and secure information processing layer for Information Exchange Requirements (IERs) (tasks, operational elements, and information flow). The security and resiliency need machine learning-based methods for targeted content dissemination, and, proactive dissemination/caching (TA1), and dynamic mission-oriented data discovery (TA2). Secure information processing in combat cloud needs efficient and dynamic fine-grained Attribute-based key distribution, verification, and revocation for group-based coordination (TA3) for a collaborative DIL environment. We will design algorithms, and develop a system prototype in a Delayed/Disconnected, Intermittently-Connected, Low-Bandwidth (DIL) environment to validate the Combat Cloud design discussed here.

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