Machine learning enabled adaptive user interfaces for enhanced network security

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Manual network monitoring is essential for network-security in the presence of increasingly sophisticated and novel forms of cyber-attack. Unfortunately, manually monitoring complex configurations of devices across different logical views of a computer network leads to an information overload when all information is visualized simultaneously. To this end, it is critical to build adaptive network visualization tools that anticipate users' needs with minimal interaction as well as triage potential network threats without overloading a user. At the core of such adaptivity are two tasks: (1) the need to perform fast inference in dynamical influence diagram models of system state and associated risks to inform interface adaptations and (2) the need to learn and continually update both the system model and the user model in order to ensure accurate inference and decision-making in part (1). We will partner with Uncharted to develop novel machine learning and inference algorithms to address both tasks (1) and (2) in a novel interface for the development of novel enhanced network security monitoring tools. While this research project will have direct application to cyber-security and network monitoring, the underlying techniques should generalize to a broad range of adaptive user interfaces.