arXiv:2605.27299v1 Announce Type: cross Abstract: 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
The increasing volume of cybersecurity alerts necessitates advanced methods to combat alert fatigue and improve response efficiency. This research addresses a critical operational bottleneck in modern security systems.
Improved alert prioritization directly enhances the effectiveness of cybersecurity defenses, a vital component of national and corporate security infrastructure. It also signals a move towards more intelligent and autonomous security operations.
The proposed framework allows for more intelligent and risk-sensitive prioritization of IDS alerts, moving beyond simplistic rule-based systems to incorporate uncertainty and organizational risk attitudes.
- · Cybersecurity companies
- · Security operations centers
- · Organizations with complex IT infrastructure
- · AI/ML in cybersecurity
- · Attackers relying on alert overload
- · Legacy IDS vendors
More efficient and effective incident response in cybersecurity.
Reduced operational costs for security teams and a potential reduction in successful cyberattacks.
Integration of similar fuzzy logic and AI-driven prioritization across other critical operational systems beyond cybersecurity.
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Read at arXiv cs.LG