The DOI matches its Crossref record.
Combining filtering and statistical methods for anomaly detection
Overview
The claims and statistics of this paper haven’t been checked yet.
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Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks
Liu, P., Xu, H., Ouyang, Q., Jiao, R., Chen, Z., Zhang, S., Yang, J., Mo, L., Zeng, J., Xue, W., et al. (2020) · 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre5003.2020.00014 · Cited by 252
Metadata
- Authors
- Soule, A., Salamatian, K., & Taft, N.
- Venue
- Proceedings of the 5th ACM SIGCOMM conference on Internet measurement - IMC '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 1
- OpenAlex
- W4245906277
- Cited by
- 223
- References
- 0
- FWCI
- 3.82
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Combining filtering and statistical methods for anomaly detection. https://app.cochat.ai/papers/10.1145/1330107.1330147
- The paper
- Soule, A., Salamatian, K., & Taft, N. (2005). Combining filtering and statistical methods for anomaly detection. Proceedings of the 5th ACM SIGCOMM conference on Internet measurement - IMC '05. https://doi.org/10.1145/1330107.1330147
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
