Predicting Tail-Risk Escalation in IDS Alert Time Series

2026-01-16

AI Summary

This study addresses the challenge of distinguishing benign network scans from high-severity attack escalations in the overwhelming volume of alerts generated by intrusion detection systems (IDS), with a focus on identifying attack trends exhibiting tail risk. To this end, the authors introduce, for the first time in cybersecurity, an extreme-state prediction methodology adapted from quantitative finance, proposing a time-series–based tail risk early-warning framework. The approach extracts features such as alert intensity, volatility, and short-term momentum via weighted moving averages and integrates extreme mechanism modeling with supervised learning for classification. Evaluated at a per-minute granularity, the model achieves 91% accuracy, 89% recall, and 98% precision. The training code is publicly released to facilitate reproducibility and interpretability analyses.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Natural Language Processing: Information Extraction

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• tail-risk escalation
• IDS alert time series
• attack pattern recognition
• temporal analysis
• early-warning signals

Innovation

• extreme-regime forecasting
• IDS alert time series
• temporal pattern analysis
• tail-risk prediction
• attack escalation detection