Anomaly Detection

Anomaly detection, also known as outlier detection, refers to the identification of patterns in a dataset that do not conform to expected behavior. These non-conforming patterns are termed anomalies or outliers, and they often provide critical and actionable information in various application domains.

How Anomaly Detection Works

Approaches to Anomaly Detection:

1. Statistical‍

Based on the assumption that the normal data follows a particular distribution and outliers deviate from this distribution.

2. Machine Learning-Based:

1. Supervised Anomaly Detection: Requires a labeled dataset containing both normal and anomalous samples. Classic algorithms like SVM, Decision Trees, or Neural Networks can be trained for classification.
2. Unsupervised Anomaly Detection: Doesn't need labeled data. Algorithms such as k-means clustering, DBSCAN, or autoencoders can be employed.
3. Semi-Supervised Anomaly Detection: Uses only normal data for training, assuming that anomalies are very rare and shouldn't significantly impact the model. One-class SVM is an example.

Distance-Based‍

Detect anomalies based on the distance of a data point from its neighbors. If a data point's distance from its neighbors exceeds a threshold, it's considered an anomaly.

Density-Based‍

Compares the density of a data point with its neighbors. Anomalies are data points in low-density regions.

Applications:

• Fraud Detection: Spotting unusual patterns in transaction data.
• Network Security: Identifying intrusions or malicious activities.
• Health Monitoring: Detecting disease outbreaks or malfunctioning machinery.
• Quality Assurance: Detecting defects in products.