Anomaly score
An anomaly score is a numerical measure of how anomalous an observation or event is relative to a reference or expected pattern.
Outlier Detection in Python
A numerical estimate of how unusual an item is relative to a specified reference group.
- How it is used
- Ranks records by estimated unusualness relative to a reference group, leaving any binary threshold to the application.
- How deep it goes
- Works through statistical, neighbor, histogram, and cluster-relative scoring, then examines score direction, thresholds, evaluation, and combination.
- Examples
- Uses fund segments, baseball nearest-neighbor scores, speech-data histograms, and FPOF and KDE score distributions.
- What you can do after
- Apply it via detector scores to rank investigation candidates, assess ranking quality, and choose thresholds cautiously.
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Azure Security
Anomaly scoring is a web application firewall evaluation method that assigns a cumulative score to detected request anomalies to determine whether traffic is blocked.
- How it is used
- Determines whether Azure WAF on Application Gateway blocks requests that match managed rules.
- How deep it goes
- Explains severity values, cumulative addition, and the prevention threshold, but does not detail individual rule logic.
- Examples
- Uses critical, error, warning, and notice scores plus a two-rule total of 3 and 2.
- What you can do after
- Operate/configure prevention behavior by interpreting rule severities and predicting when requests will be blocked.
Related concepts
- reconstruction lossequivalent to · Reconstruction loss is a measure of the discrepancy between an input or observation and the output a model reconstructs from it or its representation.
- signaluses · A signal is an observable value, event, or pattern that conveys information about a condition, state, or phenomenon.
- isolation forestproduced by · Isolation Forest is an ensemble anomaly-detection algorithm that identifies unusual observations by recursively isolating them through random partitioning.
- outlier detectionproduced by · Outlier detection is the process of identifying observations that substantially deviate from an expected or prevailing pattern in data.
- univariate scoring methodproduced by · A technique that assigns an outlier score to each observation using one feature at a time.
- ensembleused by · An ensemble is a technique that combines or considers outputs from multiple models or component methods collectively to produce or assess a joint result.
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