고급 이상 징후 탐지 기법

Welcome! In monitoring, we look for things that are out of the ordinary. These unexpected events are called anomalies.

Simple alerts are great, but sometimes we need smarter ways to spot issues that don't fit a fixed rule. That's where advanced anomaly detection comes in!

You might already use basic alerts like 'CPU usage > 90%'. These are threshold-based.

While useful, they have limits:

• They don't adapt to normal changes (e.g., peak hours).
• They miss subtle deviations within 'normal' ranges.
• They can generate lots of false alarms.

One way to go beyond fixed thresholds is using statistical methods. These techniques help us understand what 'normal' data looks like.

An anomaly is then defined as a data point that is statistically 'unlikely' or far from the expected normal behavior.

A common statistical technique is using the Z-score. It measures how many standard deviations a data point is away from the mean (average).

A high absolute Z-score (e.g., +3 or -3) suggests the data point is unusual. Think of it as a 'how weird is this value?' meter.

Z = (X - Mean) / StdDev

Most monitoring data is time-series data, meaning it changes over time. This data often has patterns:

• Seasonality: Daily, weekly, or monthly cycles.
• Trends: Gradual increases or decreases over time.

Advanced anomaly detection can spot when data breaks these natural time-based patterns.

Machine Learning (ML) takes anomaly detection to the next level. Instead of fixed rules, ML models learn what 'normal' looks like from your historical data.

This allows them to detect much more complex and subtle anomalies that statistical methods or fixed thresholds might miss.

ML models are 'trained' on a large amount of historical data that represents your system's healthy, expected behavior.

During training, the model builds a detailed profile of what is considered 'normal'. When new data comes in, it compares it to this learned profile to identify deviations.

There are many ML algorithms for anomaly detection. Here are two examples:

• Isolation Forest: Works by 'isolating' anomalies, which are usually fewer and different, making them easier to separate from normal data.
• One-Class SVM: Learns a boundary around the 'normal' data points. Anything outside this boundary is considered an anomaly.

Advanced anomaly detection is incredibly useful in production:

• Security: Detecting unusual login patterns or data access.
• Performance: Spotting abnormal spikes in latency or resource usage.
• Business Metrics: Identifying sudden, unexpected drops in user sign-ups or purchases.

While powerful, anomaly detection isn't perfect:

• False Positives: Alerting on normal events.
• False Negatives: Missing actual anomalies.
• Requires good quality, representative historical data for training.
• Can be complex to configure and fine-tune.

Great job! You've explored the world of advanced anomaly detection.

• We moved beyond basic thresholds.
• Learned about statistical methods like Z-scores.
• Discovered how ML models learn 'normal' behavior to spot complex deviations.
• Understood the practical uses and challenges.

These techniques are key to proactive monitoring!