arXiv Machine Learning
Risk-Calibrated Bayesian Streaming Intrusion Detection with SRE-Aligned Decisions
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CALIBURN: Operationally Calibrated Streaming Intrusion Detection with Regime-Dependent Conformal Risk Control
arXiv:2605. 24696v2 Announce Type: replace-cross Abstract: Streaming intrusion detection systems must process flows continuously under bounded memory, yet most leave alerting-threshold selection as a post-hoc tuning problem incompatible with production, where operators commit in advance to alert budgets, misclassification costs, and Service Level Objectives.
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Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software
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Uncertainty-Aware Sequential Decision Rules for Event-Triggered LLM Invocation in Streaming Systems
arXiv:2607. 13048v1 Announce Type: cross Abstract: Streaming inference pipelines increasingly pair lightweight fast models with Large Language Models (LLMs) that provide rich semantic understanding at substantial cost.
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How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection
arXiv:2608. 01454v1 Announce Type: cross Abstract: Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choices and evaluation protocols.
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Too Sure to Be Safe: Model Calibration for Reliable Log Anomaly Detection
The paper introduces LoRD, a lightweight post‑hoc calibration framework designed to improve confidence reliability in language‑model‑based log anomaly detectors. LoRD learns route‑specific reliability models from latent representations of correctly classified validation samples and uses reconstruction distances to estimate prediction reliability. By selectively recalibrating high‑risk predictions, LoRD reduces overconfident errors while maintaining strong anomaly detection performance across four large‑scale log benchmark datasets.