FIRCE: A Framework for Intrusion Response and Conformal Evaluation

2026-05-03

AI Summary

This study addresses the performance degradation of intrusion detection models in real-world network environments caused by concept drift. To tackle this challenge, the authors propose a robust detection framework that integrates uncertainty quantification with adaptive chunking. The core innovation lies in the design of an approximate cross-conformal evaluator combined with a dynamic chunking strategy, which enhances sensitivity to distribution shifts and computational efficiency while maintaining low calibration overhead. Experimental evaluations on a custom-built IoT platform and benchmark datasets (CICIDS2018 and UNSW-NB15) demonstrate that the proposed method effectively identifies concept drift and triggers model retraining, thereby significantly improving the adaptability and robustness of supervised intrusion detection systems.

Abstract

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.

Problem

• intrusion detection
• concept drift
• model degradation
• network traffic
• distributional shift

Innovation

• conformal evaluation
• concept drift detection
• adaptive chunking
• intrusion detection system
• Approximate Cross-Conformal Evaluator