{"ID":3049925,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-06T15:44:26.945507316Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05090","arxiv_id":"2606.05090","title":"Bernoulli CUSUM and Bayes-Optimal Detection Ceilings for Trust Fraud in Sparse Rating Networks","abstract":"Sequential trust detection in rating networks relies on continuous observation models that fail on real data. On Bitcoin-OTC, 56\\% of ratings take a single value under standard mapping, breaking the distributional assumptions that parametric detectors require. This paper makes three contributions. It derives a Bayes-optimal F1 detection ceiling for per-node sequential detectors using empirically measured observation parameters. At Bitcoin-OTC's median in-degree of 2, this ceiling falls to 0.451 for strategic attacks, explaining why unsupervised methods cluster near $F1 \\approx 0.4$. The analysis shows that detector-model matching, not information content, determines performance: binary models retain 86\\% of mutual information while enabling exact parametric fit. A dual-regime architecture is presented where Bernoulli CUSUM detects behavioral shifts and triggers asymmetric scoring. Ablation reveals a co-design constraint: the modulation mechanism improves AUC by 0.030 on binary observations but degrades it by 0.094 on continuous observations. The combined system achieves AUC 0.749 on Bitcoin-OTC and 0.796 on Bitcoin-Alpha, beating GaaSTrust on all 8 attacks ($p \u003c 0.003$), with founder-label AUC of 0.999.","short_abstract":"Sequential trust detection in rating networks relies on continuous observation models that fail on real data. On Bitcoin-OTC, 56\\% of ratings take a single value under standard mapping, breaking the distributional assumptions that parametric detectors require. This paper makes three contributions. It derives a Bayes-op...","url_abs":"https://arxiv.org/abs/2606.05090","url_pdf":"https://arxiv.org/pdf/2606.05090v1","authors":"[\"Talal Ashraf Butt\"]","published":"2026-06-03T16:56:15Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.SI\"]","methods":"[]","has_code":false}