CALIBURN: A Regime-Sensitivity Study of Operationally Calibrated Streaming Intrusion Detection
arXiv:2605.24696v1 Announce Type: cross Abstract: Streaming network intrusion detection systems must process flows continuously while keeping memory bounded, but most current methods leave alerting threshold selection as a post-hoc tuning problem poorly suited to production. Operators need alerting behaviour specifiable before deployment using inputs such as false-negative cost, false-positive cost, and alerting budget. This paper presents CALIBURN, a five-component streaming alerting pipeline composed of a truncated Bayesian online change-point detector, an isotonic calibration layer mapping
The increasing sophistication and scale of cyber threats require more robust and adaptable intrusion detection systems, pushing research towards operationally optimized solutions.
This research addresses a critical gap in network intrusion detection by allowing for pre-deployment specification of alerting behavior, directly impacting the effectiveness and efficiency of cybersecurity operations.
The ability to define alerting thresholds based on operational costs (false-negative, false-positive) and budgets before deployment will fundamentally change how streaming intrusion detection systems are configured and managed.
- · Cybersecurity providers
- · IT security departments
- · Critical infrastructure operators
- · Threat actors
- · Legacy IDS vendors
Improved network security posture and reduced operational overhead for incident response teams.
Increased adoption of AI-driven, adaptive security tools across industries due to enhanced reliability and control.
A potential shift in cyber insurance models, linking premiums to the sophistication and operational calibration of an organization's intrusion detection capabilities.
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Read at arXiv cs.LG