Risk Averse Alert Prioritization for IDS Using Subnormal Gaussian Fuzzy Models
Modern intrusion detection systems generate thousands of alerts daily, but alert fatigue severely limits security operations effectiveness due to too many false positives or low-impact events. We address this by proposing a principled framework for alert prioritization based on subnormal Gaussian fuzzy numbers, explicitly modeling three sources of uncertainty: threat severity, detection confidence, and organizational risk attitude. Each alert is represented as a fuzzy number with the core indicating severity, spread indicating uncertainty, and height reflecting detection reliability. We apply ranking indices to prioritize alerts, allowing organizations to tune security posture through a r...
| Authors | Murat Moran |
|---|---|
| Primary category | cs.CR |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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