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Sumo Logic

Use Cases for Anomaly Detection vs Outlier

Example Log Messages

The following log messages are examples that would work well with Anomaly Detection, containing repeated structure:

$DATE %PIX-6-*: access-list outside_access_in denied tcp outside/*(*) -> inside/*(*)


$DATE StockTraderWebApplicationServiceClient.sell INFO: Stock Ticker Update Successful.

This next example is unstructured. If your messages are similar, use the Outlier operator to find anomalies. - - [Wed May 13 20:05:36 UTC 2015] 
"GET /_includes/wp/blog/wp-content/plugins/us/31063765-bpfull.phpi?&w=50&id=6&random=1331063765 HTTP/1.1" 
200 6677 "" "Mozilla/5.0 
(iPhone; U; CPU iPhone OS 4_1 like Mac OS X; en-us) AppleWebKit/532.9 (KHTML, like Gecko) 
Version/4.0.5 Mobile/8B5097d Safari/6531.22.7"

Outlier Query Examples

Using the Outlier operator to determine anomalies can be more flexible, but requires more effort. You can use any query that transforms logs into a time-series, for example:

Parsing out a numerical field.

Use the following query to parse out a numerical field.

... | parse "latency=*," as latency
| timeslice 5m
| avg(latency) by _timeslice
| outlier _avg

Counting logs.

You can use the following query to count logs.f

"transaction processed"
| timeslice 5m
| count by _timeslice
| outlier _count

For more information, see Outlier operator.