What question did this study set out to answer?

This survey aims to provide a comprehensive overview of anomaly detection techniques across different domains.

July 1, 2009ACM Computing Surveys

Anomaly detection

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Authors

VCVarun ChandolaBuffalo State University ABArindam BanerjeeDharmsinh Desai University VKVipin KumarJain University

Discussion

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Implication

Survey categorizes anomaly detection techniques, assessing their effectiveness and computational challenges in various domains, suggesting broader applicability.

Key Points

This survey aims to provide a comprehensive overview of anomaly detection techniques across different domains.
Grouped techniques into categories based on their underlying approaches.
Identified key assumptions differentiating normal and anomalous behavior for each technique.
Discussed computational complexity and advantages/disadvantages of techniques on a category basis.
Provided a structured template for understanding various techniques in each category.
Highlighted how techniques from one domain can be applied to others, improving adaptability.
Outlined the computational challenges faced by different anomaly detection methods.

Cite This Study

Chandola et al. (2009) studied this question.

synapsesocial.com/papers/69d689de4ebc9853a6db8274 https://doi.org/10.1145/1541880.1541882

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Implication

Survey categorizes anomaly detection techniques, assessing their effectiveness and computational challenges in various domains, suggesting broader applicability.

Anomaly detection

Discussion

Member takes

Implication

Key Points

Cite This Study

Anomaly detection

Discussion

Member takes

Implication

Key Points

Cite This Study

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