# FIRCE: A Framework for Intrusion Response and Conformal Evaluation

arXiv: https://arxiv.org/abs/2605.01962 · HTML: https://mlsanity.com/paper/2605.01962/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2026.

## Built on by (most cited first, 1 of 1)

- [Stream Assembly Is an Uncontrolled Treatment in Streaming Intrusion-Detection Benchmarks](https://mlsanity.com/paper/2605.24696/) (2026)

## Abstract (arXiv)

Machine learning-based intrusion detection systems deployed in real-world environments frequently suffer from model degradation due to concept drift, where changes in traffic patterns invalidate training assumptions. To address this, we present FIRCE, a Framework for Intrusion Response and Conformal Evaluation that augments supervised IDS classifiers with conformal evaluation-based uncertainty quantification and drift detection. FIRCE supports four conformal evaluation strategies: Inductive, Cross, Approximate Transductive, and our proposed Approximate Cross-Conformal Evaluator, which achieves robust performance with minimal calibration overhead. FIRCE also introduces an adaptive chunking mechanism that dynamically adjusts evaluation granularity in response to stream volatility, improving drift responsiveness while preserving computational efficiency. Using a custom IoT testbed of 10 commercial devices and time-series network captures under simulated attack and drift conditions, we demonstrate FIRCE's ability to detect distributional shifts and trigger model retraining. We additionally benchmark FIRCE on the CICIDS2018 and UNSW-NB15 datasets to validate its generalizability. Experimental results show that conformal evaluation-based drift detection, combined with adaptive chunking, enables an efficient and robust response to evolving threats.