Risk-Calibrated Bayesian Streaming Intrusion Detection with SRE-Aligned Decisions

Streaming intrusion-detection studies assemble evaluation streams from network captures by interleaving capture days, pooling captures, or replaying records round robin. We show on two benchmarks that this assembly is an uncontrolled experimental treatment changing what the evaluation measures. On CICIDS2017, reordering an identical record multiset under a fixed positional 70/15/15 split yields held-out samples sharing only 32.5% of their records, at prevalences of 68.235% and 25.2396% (42.9954 points apart), and reverses the measured ordering of the two deterministic scorers. Restricting both arms to the 78000 records both held out removes the reversal, so it is attributable to which records the assembly hands to the test set, not to the order in which the detector saw its history. That attribution assumes that history contributes no more on the records the arms do not share than on those they do. On LITNET-2020, pooling three temporally disjoint captures reports one 6.4982% operating point, the equal-weight mean of per-capture held-out prevalences from 0.176% to 15.7747%, an identity presented as an audit check. The evaluated detector's reset posterior P(r_t=0) equals the hazard rate exactly below the run-length cap, though evaluations spend nearly all their length at or beyond it, and its evaluated score is a function of P(r<=5), not of P(r=0). Its deployed max composition ranks worse than its tail term alone (0.103477 AP, 0.302658 AUC-ROC) because the auxiliary branch is inverted (AUC-ROC 0.281890) and the maximum lets it set the score wherever the tail is small. With evaluated records and fitted model fixed, changing only the accompanying batch moves the ECOD reference implementation's AUC-PR by 0.003063, so published ECOD numbers are not comparable across studies scoring different batches. Every measured value traces to an archived, hash-verified run manifest.

Provenance-Based Intrusion Detection Systems (PIDSs) detect Advanced Persistent Threats (APTs) by analyzing system interactions. However, existing methods largely treat relations uniformly, overlooking statistical heterogeneity; in CADETS, relation frequencies differ by approximately $140{,}000\times$. This may cause PIDSs to focus more on frequent relations and overlook differences in normal error levels across relations, increasing the risk of false alarms and missed detections. We present RECAL, an unsupervised framework using relation-balanced masked graph learning to better capture rare interaction patterns. It further calibrates reconstruction errors against each relation's benign error distribution to produce comparable anomaly evidence, helping distinguish attacks from benign behavior and reduce false alarms. On three DARPA E3 datasets, RECAL achieves F1 scores of 99.99%, 99.93%, and 99.99%, outperforming the best baseline on each dataset by 0.88, 0.82, and 0.42 percentage points, respectively. Compared with the baseline reporting the lowest FPR, RECAL reduces mean FPR by approximately $105\times$, $4\times$, and $41\times$.

Intrusion detection systems are often trained under static benchmark conditions, although deployed network environments are affected by traffic drift, sensor noise, changing workloads, and evolving attack behaviour. Under such distribution shifts, static detectors may produce confident but incorrect predictions, leading to silent and unsafe failure modes. In this paper, RES-DARE (Recursive Evolving Specialists-Digital Adaptive Reasoning Engine) is proposed as a failure-aware continual intrusion detection framework with rollback-safe self-repair. Difficult, uncertain, and misclassified samples are treated as failure signals for expert specialisation rather than being discarded as noise. A supervised contrastive encoder, two-pass expert router, failure-buffer mechanism, HDBSCAN-based failure-region discovery, and trust-risk monitor are integrated to support adaptive IDS behaviour. AEHM-v2 is introduced as a rollback-safe repair mechanism, where candidate adaptations are provisionally activated and committed only when macro-F1 is preserved or improved while trust risk remains stable. Otherwise, the system is rolled back to its last validated state. RES-DARE is evaluated on CICIDS2017, UNSW-NB15, and TON_IoT, achieving macro-F1 scores of 0.9850, 0.9736, and 0.9691, respectively. Under Gaussian feature corruption at strength 0.10, RES-DARE retains an Attack-F1 of 0.7920 on CICIDS2017 and achieves near-zero catastrophic forgetting with F = 0.0015. The results show that RES-DARE improves robustness, warning capability, and deployment safety under degraded conditions.