The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Anomaly Detection: A Survey
Overview
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Citations
Cites
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
Goldstein, M., & Uchida, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0152173 · Cited by 1,032
Cited by
Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., & Chawla, N. V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011409 · Cited by 855
Toward Supervised Anomaly Detection
Goernitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3623 · Cited by 371
NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage
Hassan, W. U., Guo, S., Li, D., Chen, Z., Jee, K., Li, Z., & Bates, A. (2019) · Proceedings 2019 Network and Distributed System Security Symposium · doi:10.14722/ndss.2019.23349 · Cited by 341
Outlier Detection: Methods, Models, and Classification
Boukerche, A., Zheng, L., & Alfandi, O. (2021) · ACM Computing Surveys · doi:10.1145/3381028 · Cited by 334
IoT anomaly detection methods and applications: A survey
Chatterjee, A., & Ahmed, B. S. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100568 · Cited by 323
Metadata
- Authors
- Musa, T. H. A., & Bouras, A.
- Venue
- Lecture Notes in Networks and Systems
- Publisher
- Springer Singapore
- Published
- 2021-10-27
- Pages
- 391-401
- ISSN
- 2367-3370 (ISSN-L)
- 2367-3389
- OpenAlex
- W3208688400
- Cited by
- 958
- References
- 3
- FWCI
- 168.16
- Type
- book chapter
- Language
- en
- Open access
- closed
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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.
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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: Anomaly Detection: A Survey. https://app.cochat.ai/papers/10.1007/978-981-16-2102-4_36
- The paper
- Musa, T. H. A., & Bouras, A. (2022). Anomaly Detection: A Survey. Lecture Notes in Networks and Systems. https://doi.org/10.1007/978-981-16-2102-4_36
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.
