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.

Anomaly Detection