Log Anomaly Detection Starter
Detect unusual patterns in logs with simple methods.
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Help me detect unusual patterns in logs with simple methods first. Log type and sample lines: [LOG_SAMPLES]. Volume and retention: [VOLUME_AND_RETENTION]. What counts as a problem for us: [PROBLEM_DEFINITION]. Language and tools: [LANGUAGE_AND_TOOLS]. Take the problem through this tree and stop at the simplest approach that works: 1. Can the problem be caught by a rule? (error level counts, specific messages, status codes, login failures). If yes, write the rules and thresholds, and alert on rates, not single events. 2. Is the issue a change in volume or rate? Use a baseline by hour of week (median and a robust spread measure) and flag values outside a band; show the calculation. 3. Is it a new or rare message pattern? Normalize messages into templates (replace numbers, ids, and timestamps), count templates per period, and flag templates never seen before or with a sudden rise. 4. Is it a combination of fields (user, IP, endpoint)? Consider simple statistical outlier methods per entity. 5. Only then consider machine learning, and explain why it may not be worth the maintenance. Deliver code for steps 1 to 3, a way to evaluate (label known incidents and measure detection and false alarms), an alert design that avoids fatigue (grouping, suppression, severity), and a tuning routine. Be clear about limits.
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