Anomaly Detection

Simple Definition for Beginners:

Anomaly detection is the process of identifying unusual patterns or behaviors in data that do not conform to expected norms.

Common Use Example:

In credit card fraud detection, anomaly detection systems identify transactions that are unusual compared to a customer’s typical spending behavior, such as a sudden large purchase in a foreign country.

Technical Definition for Professionals:

Anomaly detection involves identifying data points, events, or observations that deviate significantly from the expected pattern of a given dataset. It is used in various fields such as fraud detection, network security, and fault detection. Key aspects and methods of anomaly detection include:

• Statistical Methods: Using statistical models to identify anomalies based on deviation from a statistical norm or distribution.

Z-score: Measures how many standard deviations a data point is from the mean.

Grubbs’ Test: Identifies outliers in a dataset assumed to be normally distributed.

• Machine Learning Approaches: Utilizing algorithms to learn from data and detect anomalies.

Supervised Learning: Requires labeled training data with known anomalies.

Unsupervised Learning: Identifies anomalies in unlabeled data, commonly using clustering and density estimation.

Semi-supervised Learning: Combines labeled normal data with unlabeled data for anomaly detection.

• Proximity-Based Methods: Detecting anomalies based on their distance from other data points.

K-Nearest Neighbors (KNN): Identifies anomalies based on the distance to the nearest neighbors.

Local Outlier Factor (LOF): Measures the local density deviation of a data point compared to its neighbors.

• Information-Theoretic Methods: Using information theory measures such as entropy to detect anomalies.
• Spectral Methods: Identifying anomalies using eigenvalue analysis and principal component analysis (PCA).

Anomaly detection systems are essential for identifying and responding to unexpected events, ensuring the integrity of systems, data, and processes.