Anomaly Detection¶
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly Detection: A Survey. ACM, 41(3), 1-58.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Expectation Violation
- Practitioners tune these systems by moving the threshold rather than the model, which is the same lever the structure names.
This sourceDescribes scoring each instance against a model of normal behaviour and states that the analyst selects anomalies by applying a domain-specific cutoff threshold to that score.
- Practitioners tune these systems by moving the threshold rather than the model, which is the same lever the structure names.
- Monitoring
- This contextual interpretation is built into sophisticated monitoring systems (e.g., SLOs that account for seasonal demand, anomaly detectors trained on system-specific baselines), and Chandola, Banerjee, and Kumar (2009) survey how anomaly-detection algorithms across domains formalize this context-relative notion of "normal."
This sourceComprehensive cross-domain survey of anomaly-detection methods; formalizes how context (point, contextual, collective anomalies) determines what counts as deviation, supporting comparative reasoning in monitoring system design.
- This contextual interpretation is built into sophisticated monitoring systems (e.g., SLOs that account for seasonal demand, anomaly detectors trained on system-specific baselines), and Chandola, Banerjee, and Kumar (2009) survey how anomaly-detection algorithms across domains formalize this context-relative notion of "normal."
Verification¶
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