Abstract
<title>Abstract</title> <p>Prior to the system-level visibility of service degradation, network traffic may experience gradual structural changes as a result of burst activity, dispersion shifts, or adversarial pressure. Though they are able to spot outliers, traditional methods of monitoring that rely on statistical tests, classification rules, or thresholds cannot measure how close you are to a crucial limit or explain how traffic distributions turn unstable. A mathematical framework for stability-domain operations is described in this study. A feature space is used to represent each traffic window as a normalised condition with respect to a benign reference regime. Instead of depending on empirical weighting or classifier aggregation, a stability functional is built to assess the distance of the current state from a theoretically specified stability domain, and a distortion operator is designed to capture location, variance, and scale differences. A formulation that satisfies nonnegativity, monotonicity, dominant-feature behaviour, and which additionally provides an interpretation of collapse boundaries and a framework for feature-wise contributions is proven analytically. Both real-world flow-level DDoS traffic validation and Monte Carlo experiments demonstrate that the suggested solutions systematically improve in the face of adversarial and structural perturbations. Consequently, the framework offers a comprehensible early-warning sign for having focus on distributions of network traffic that can indicate structural instability.</p>