Risk Averse Alert Prioritization for IDS Using Subnormal Gaussian Fuzzy Models

Researchers propose a fuzzy logic framework for prioritizing intrusion detection system alerts by modeling uncertainty in threat severity, detection confidence, and organizational risk tolerance. The method significantly outperforms baseline systems under detector degradation, offering security teams a more robust approach to managing alert fatigue.

Alert fatigue represents a critical vulnerability in modern cybersecurity operations. Security teams face overwhelming volumes of daily alerts, many of which are false positives or low-priority events, causing analysts to miss genuine threats through sheer noise. This research addresses a genuine operational challenge that impacts every organization running intrusion detection systems by proposing a mathematically principled approach to alert triage.

The framework leverages subnormal Gaussian fuzzy numbers—a mathematical construct that elegantly captures three simultaneous dimensions of uncertainty inherent in threat detection. Rather than treating alerts as binary true/false events, this approach models the core of each alert's severity, the spread of uncertainty around that estimate, and the confidence level in detection itself. This multi-dimensional representation better reflects real-world security dynamics where confidence in threat assessment varies considerably.

The empirical validation on industry-standard datasets (CIC-IDS2017 and NSL-KDD) demonstrates material improvements, particularly under realistic conditions where detectors degrade or miscalibrate. Achieving 0.9963 versus 0.8215 normalized discounted cumulative gain represents substantial performance differentiation. Organizations gain the ability to adjust their security posture through a configurable risk-attitude parameter, enabling context-specific tuning rather than one-size-fits-all alert handling.

For security operations centers, this framework offers computational efficiency paired with interpretability—critical factors for adoption. Unlike black-box approaches, analysts can understand the reasoning behind prioritization decisions. The robustness across different detector families and failure modes addresses a practical deployment concern often overlooked in academic research.

• →Subnormal Gaussian fuzzy models capture three uncertainty dimensions simultaneously: threat severity, detection confidence, and risk attitude.
• →Framework achieves 21% performance improvement over baselines under detector degradation scenarios relevant to production environments.
• →Tunable risk-attitude parameter allows organizations to customize alert prioritization without model retraining.
• →Method maintains computational efficiency while providing interpretable decision reasoning for security analysts.
• →Validation on CIC-IDS2017 and NSL-KDD demonstrates robustness across detector families and miscalibration conditions.