Self supervised traffic embedding for high resolution risk assessment in large scale campus networks

High-resolution risk assessment in large-scale campus networks is essential for maintaining network security and operational efficiency. Traditional methods often struggle to capture the intricate temporal and spatial dynamics inherent in network traffic, leading to suboptimal risk predictions and limited interpretability. This paper presents a novel methodological framework that leverages self-supervised traffic embedding models to address these challenges. The framework is composed of three core components: a robust mathematical foundation for traffic embedding and risk quantification, the counterfactual temporal forecaster integrating a manifold constrained risk encoder, an interaction-aware risk event extraction, and a probabilistic outcome regularizer to encode, segment, and refine risk predictions, and a counterfactual temporal risk refinement strategy to explore hypothetical traffic scenarios and enhance prediction robustness under uncertainty. These components collectively enable precise and interpretable risk predictions, effectively addressing the variability and complexity of campus network traffic. Experimental evaluations validate the proposed methodology, demonstrating its capability to deliver high-resolution risk assessments with significant improvements in accuracy and interpretability. The results underscore the potential of this framework to advance network management and security in large-scale environments, offering a scalable and reliable solution for modern campus networks.