Interactive machine learning for identifying threats to security and service in large scale mobile networks
James Barrett (2026)
Abstract
The application of machine learning technologies for predictive analytics in time series data is an expansive and continually evolving field, in concern to both real, and non-real time environments and networks. Ensuring effective security alerting practices and providing consistent service delivery with machine learning-based services has emerged as a research -competitive and continually advancing area, with balance of both factors proving to be a developing challenge. Existing research in interactive, and explainable based machine learning systems to serve service needs and cybersecurity purposes has seen significant growth, however also dawning concerns and challenges to performance based needs and privacy considerations. Largely unexplored is the adaptive, and interactive usage of novel interactive and explainable machine learning methods to continually adapt and serve the needs of predictive systems and networks, deterring threats preemptively and ensuring the service and safety to large scale systems, of which our application area is telecommunications data.