Robust Conformal Intrusion Detection via Traffic-Aware Calibration and Attack-Orbit Invariance
Large language models fine-tuned for network intrusion detection emit single-point predictions without statistical validity guarantees. Conformal prediction supplies a finite-sample coverage guarantee, but a threshold calibrated on clean traffic fails once an adversary perturbs controllable network features. We demonstrate this failure across three intrusion detection benchmarks and propose traffic-aware conformal prediction, which calibrates on traffic drawn from the perturbation mechanism an attacker is expected to use and provably restores coverage whenever that mechanism is known and can be sampled. A stronger, adaptive attacker that queries the target model's own score can still degr...
| Authors | Zhenpeng Li |
|---|---|
| Primary category | cs.CR |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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