Candidate Comparability Before Promotion: Conditional Validation in Adaptive Network Intrusion Detection

• URL: http://arxiv.org/abs/2609.04388v1
• Date: Thu, 03 Sep 2026 18:55:09 GMT
• Title: Candidate Comparability Before Promotion: Conditional Validation in Adaptive Network Intrusion Detection
• Abstract summary: Promotion is security-relevant because it changes the model responsible for subsequent attack detection.<n>We test dependence on CICIDS 2017, UNSW-NB15 and ToN-IoT with self-contained challenger pipelines.<n>Raising nominal candidate evidence from 512 to 2,000 samples per class improved promotion under pool-constructed progressive drift by +0.53, +1.67 and +0.38.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive network intrusion detection systems retrain classifiers after drift alarms, but an alarm detects change; it does not establish that a challenger should replace the deployed incumbent. Promotion is security-relevant because it changes the model responsible for subsequent attack detection, and evaluating it has a methodological problem: promotion conclusions may depend on how the challenger was constructed and on how much evidence supports it. We test that dependence on CICIDS2017, UNSW-NB15 and ToN-IoT with self-contained challenger pipelines, nested candidate-size controls, a common-harness comparison of nine update policies, and a final sensitivity confining every exact feature vector to one evaluation, training or probe role. Incumbent-owned frozen preprocessing amplified apparent promotion harm; with self-contained challenger pipelines the mean full-drift harm did not persist. Raising nominal candidate evidence from 512 to 2,000 samples per class improved promotion under pool-constructed progressive drift by +0.53, +1.67 and +0.38 balanced-accuracy points: positive and statistically resolved in all three benchmarks, but materially benchmark-dependent rather than homogeneous, and driven mainly by fewer false positives. Policy conclusions were partially robust: policy ordering changed with candidate comparability, no policy globally dominated, and earlier compatibility statements for a label-free estimator and a calibrated ensemble narrowed. Validation helped evidence-disadvantaged challengers but added no average benefit at parity. Thirteen replays on real, time-ordered traffic showed no net harm from always deploying. Challenger construction and evidence should be controlled, reported and interpreted explicitly when promotion is evaluated.

Related papers

• SafeRestore: Detector-Relative Risk Certificates for Selective Industrial Image Restoration [4.906746849209501]
  Industrial inspection pipelines often restore a measured image before a detector acts on it, yet restoration can suppress detector-supported defect structure or create clean-region activations.<n>We formulate restoration as a selective action problem over the measured display, five restored candidates, and review.<n>SafeRestore ranks candidates with action-specific fitted scores, chooses a gate on threshold-tuning data, and evaluates the fixed gate on a disjoint certification sample with two one-sided exact binomial bounds.
  arXiv  Detail & Related papers  (2026-09-03T07:32:42Z)
• FlowBalance: Verifier-Grounded Self-Improvement from On-Policy Reasoning Experience [18.64380706071327]
  FlowBalance is a verifier-grounded self-improvement method that learns a normalized distribution over complete responses.<n>For each on-policy trajectory, a frozen training-time view of the same policy uses privileged context to produce token-level log-probability gains.<n>Our analysis establishes within-group contrast preservation, a minimum-change reverse-KL characterization, monotonic verifier control of target reward, and an exact correction against false-positive self-guidance on rejected responses.
  arXiv  Detail & Related papers  (2026-09-03T00:47:11Z)
• Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents [0.0]
  Current agent systems decide delegation before reasoning begins.<n>We study a third regime: an agent that recognises, during its own reasoning, that it is unlikely to succeed.
  arXiv  Detail & Related papers  (2026-08-25T05:35:07Z)
• SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute [62.3458279176813]
  Self-Verifying Refinement is an oracle-free multi-turn reinforcement learning framework.<n>It learns to use self-verification as a compute-control policy.<n>It achieves a macro-average accuracy of 0.563 with only 2.99 inference turns on average.
  arXiv  Detail & Related papers  (2026-07-30T16:20:58Z)
• Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations [42.90321348046055]
  We propose an annotation-free attribution regularization framework based on submodular search over image regions.<n>By measuring how candidate subsets affect model outputs, the search extracts compact, class-discriminative evidence as search-derived supervision.<n> Experiments on ImageNet-100 show that our method substantially improves attribution stability, Insertion, and Deletion on ViT-B/16 with only a 0.28-point accuracy drop.
  arXiv  Detail & Related papers  (2026-07-26T20:41:38Z)
• PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception [75.70273182182397]
  PerceptionRubrics is a rubric-based evaluation framework.<n>It addresses the gap between saturated benchmark scores and real-world brittleness.<n>It pairs 1,038 information-dense images with over 10,000 instance-specific rubrics.
  arXiv  Detail & Related papers  (2026-06-26T17:59:15Z)
• PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents [0.0]
  Self-evolving agents improve by repeatedly proposing changes to their own prompts, skills, ormodifications.<n>On Qwen2.5 agents (0.5B-3B) self-evolving at the prompt level on GSM8K, SVAMP, and ARC-Challenge, greedy acceptance commits 30-42% false and 10-33% harmful edits.<n> PACE commits the real one and essentially nothing else, matching greedy's held-out accuracy at sharply lower variance and about 18% lower evaluation cost.
  arXiv  Detail & Related papers  (2026-06-06T11:12:11Z)
• Evaluating Prompt Injection Defenses for Educational LLM Tutors: Security-Usability-Latency Trade-offs [51.56484100374058]
  guardrail design entails explicit trade-offs among adversarial robustness, benign-task usability, and response latency.<n>We evaluate a domain-specific multi-layer safeguard pipeline combining deterministic pattern filters, structural validation, contextual sandboxing, and session-level behavioral checks.<n>Results expose operational trade-offs: NeMo reaches 0 percent bypass at 16.22 percent FPR and roughly 1.5s latency, while Prompt Guard yields 38.48 percent bypass with 3.60 percent FPR.
  arXiv  Detail & Related papers  (2026-03-29T18:52:01Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Preventing the Collapse of Peer Review Requires Verification-First AI [49.995126139461085]
  We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth.<n>We formalize two forces that drive a phase transition toward proxy-sovereign evaluation.
  arXiv  Detail & Related papers  (2026-01-23T17:17:32Z)
• Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation [122.83280749890078]
  We propose an improved certified defense against general poisoning attacks, namely Finite Aggregation. In contrast to DPA, which directly splits the training set into disjoint subsets, our method first splits the training set into smaller disjoint subsets. We offer an alternative view of our method, bridging the designs of deterministic and aggregation-based certified defenses.
  arXiv  Detail & Related papers  (2022-02-05T20:08:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.