Predicting Tail-Risk Escalation in IDS Alert Time Series

Abstract

Network defenders face a steady stream of attacks, observed as raw Intrusion Detection System (IDS) alerts. The sheer volume of alerts demands prioritization, typically based on high-level risk classifications. This work expands the scope of risk measurement by examining alerts not only through their technical characteristics but also by examining and classifying their temporal patterns. One critical issue in responding to intrusion alerts is determining whether an alert is part of an escalating attack pattern or an opportunistic scan. To identify the former, we apply extreme-regime forecasting methods from financial modeling to IDS data. Extreme-regime forecasting is designed to identify likely future high-impact events or significant shifts in system behavior. Using these methods, we examine attack patterns by computing per-minute alert intensity, volatility, and a short-term momentum measure derived from weighted moving averages. We evaluate the efficacy of a supervised learning model for forecasting future escalation patterns using these derived features. The trained model identifies future high-intensity attacks and demonstrates strong predictive performance, achieving approximately 91\% accuracy, 89\% recall, and 98\% precision. Our contributions provide a temporal measurement framework for identifying future high-intensity attacks and demonstrate the presence of predictive early-warning signals within the temporal structure of IDS alert streams. We describe our methods in sufficient detail to enable reproduction using other IDS datasets. In addition, we make the trained models openly available to support further research. Finally, we introduce an interpretable visualization that enables defenders to generate early predictive warnings of elevated volumetric arrival risk.