Cited by 683Network Security and Intrusion DetectionAnomaly Detection Techniques and ApplicationsArtificial Immune Systems Applications

A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data

Eskin, Eleazar, Arnold, Andrew, Prerau, Michael, Portnoy, Leonid, Stolfo, Sal·2002·Advances in Information Security

Integrity signals

Registry

Crossref · 10.1007/978-1-4615-0953-0_4

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • KNN operates only at the sample level.

    “but it operates only at the sample level.”

    From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [72]

  • K-nearest neighbors (KNN) is a widely used algorithm for unsupervised anomaly detection.

    “K nearest neighbors (KNN) [72] is a widely used algorithm for unsupervised anomaly detection”

    From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [72]

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Authors
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., & Stolfo, S.
Affiliations
Columbia University (US)
Venue
Advances in Information Security
Publisher
Springer US
Published
2002
Pages
77-101
ISSN
1568-2633 (ISSN-L)
OpenAlex
W1554085250
Cited by
683
References
32
Citations by year
2026: 10 · 2025: 21 · 2024: 26 · 2023: 29 · 2022: 31 · 2021: 43 · 2020: 45 · 2019: 45 · 2018: 40 · 2017: 44 · 2016: 33 · 2015: 33 · 2014: 48 · 2013: 21 · 2012: 23
FWCI
24.60
Type
book chapter
Language
en
Open access
closed
Locations
Advances in information security (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
Keywords
unsupervised anomaly detection, intrusion detection, geometric framework, feature maps, system call traces, network connections, sparse regions, KDD Cup 1999 dataset

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.

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