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.
[Corrected v2: an audit found that the score, threshold, and latency descriptions below are not what the shared codebase implements, and that the evaluation streams are assembled constructions. See the correction note on the title page and the corrected companion work, arXiv:2605.24696 (corrected v3), artifact doi:10.5281/zenodo.22673735.] We present a risk-calibrated approach to streaming intrusion detection that couples Bayesian Online Changepoint Detection (BOCPD) with decision thresholds aligned to Site Reliability Engineering (SRE) error budgets. BOCPD provides run-length posteriors that adapt to distribution shift and concept drift; we map these posteriors to alert decisions by optimizing expected operational cost under false-positive and false-negative budgets. We detail the hazard model, conjugate updates, and an O(1)-per-event implementation. A concrete SRE example shows how a 99.9% availability SLO (43.2 minutes per month error budget) yields a probability threshold near 0.91 when missed incidents are 10x more costly than false alarms. We evaluate on the full UNSW-NB15 and CIC-IDS2017 benchmarks with chronological splits, comparing against strong unsupervised baselines (ECOD, COPOD, and LOF). Metrics include PR-AUC, ROC-AUC, Brier score, calibration reliability diagrams, and detection latency measured in events. Results indicate improved precision-recall at mid to high recall and better probability calibration relative to baselines. We release implementation details, hyperparameters, and ablations for hazard sensitivity and computational footprint. Code and reproducibility materials will be made available upon publication; datasets and implementation are available from the corresponding author upon reasonable request.
Modern intrusion detection systems generate thousands of alerts daily, but alert fatigue severely limits security operations effectiveness due to too many false positives or low-impact events. We address this by proposing a principled framework for alert prioritization based on subnormal Gaussian fuzzy numbers, explicitly modeling three sources of uncertainty: threat severity, detection confidence, and organizational risk attitude. Each alert is represented as a fuzzy number with the core indicating severity, spread indicating uncertainty, and height reflecting detection reliability. We apply ranking indices to prioritize alerts, allowing organizations to tune security posture through a risk-attitude parameter. Experimental validation on CIC-IDS2017 and NSL-KDD demonstrates greater robustness than baselines under detector degradation (0.9963 vs 0.8215 NDCGrel@100), with distinct differentiation in mid-confidence alerts and near-parity with baselines under robust detectors. The framework is theoretically grounded, computationally efficient, provides interpretable reasoning, and remains robust across detector families and miscalibration scenarios.
False alarms remain a major barrier to deploying network intrusion detection systems (NIDS). In high-volume environments, even a sub-1% false positive rate can generate tens of thousands of daily alerts. Filter-based feature selection is attractive because it operates upstream of the classifier and adds no inference-time cost. However, classical filters use class-symmetric criteria that ignore the asymmetry of intrusion detection, where benign traffic defines the baseline and attacks are deviations from it. A recent class-asymmetric filter, Classwise Mean Deviation (CMD), addresses this issue but anchors its score to a global mean that shifts toward attack distributions under class imbalance, weakening the deviations it aims to capture. We propose Benign-Anchored Ranking and Selection (BARS), a two-stage filter that replaces CMD's global anchor with the benign-class mean and applies an order-preserving decorrelation step. We evaluate BARS on CICIDS2017, CICDDoS2019, and UNSW-NB15 using feature budgets k = {5, 10, 20, 30, 40}. On attack-majority datasets, where global-anchor bias is strongest, BARS reduces false positive rate relative to CMD by 15.4% on UNSW-NB15 at k = 20 and by 21% to 23% on CICDDoS2019 at small feature budgets while preserving true positive rate and macro-F1. On benign-majority data, BARS and CMD converge, consistent with the theoretical limit where global- and benign-anchored scores coincide. BARS is a principled refinement of CMD rather than a universally dominant filter. Although Pearson Correlation and Mutual Information often achieve lower false positive rates, they exceeded 1 TB of memory on the largest benchmarks in our evaluation. BARS retains linear-time scoring and a low memory footprint, making it suitable for resource-constrained deployments.