What is Anomaly Detection?

Anomaly detection (also called outlier detection) is the process of identifying data points, events, or observations that deviate significantly from the expected pattern or behavior. It's crucial for security, fraud prevention, and operational monitoring.

Types of Anomalies

Point Anomalies Single data points that are anomalous. Example: Unusually large transaction.

Contextual Anomalies Anomalous in a specific context. Example: High temperature reading in winter.

Collective Anomalies Groups of related data points. Example: Sequence of suspicious login attempts.

Detection Methods

Statistical Methods

Z-score
IQR (Interquartile Range)
Gaussian distribution

Machine Learning

Isolation Forest
One-Class SVM
Autoencoders
DBSCAN clustering

Deep Learning

LSTM networks
Variational Autoencoders
GANs for anomaly detection

Use Cases

Security

Intrusion detection
Fraud detection
Insider threat detection

Operations

System monitoring
Predictive maintenance
Quality control

Business

Unusual transactions
Customer behavior changes
Market anomalies