Anamoly Detection

TL;DR

• Identifies data patterns or events that deviate from expected behavior.
• Used to flag potential problems (e.g., fraud, security breaches, equipment faults) for further investigation.
• Applied across domains such as finance, network security, and manufacturing.

Definition

Anomaly detection is a technique used to identify unusual patterns or events in data that do not conform to expected behavior. These anomalous patterns can indicate a variety of issues, such as fraud, security breaches, or technical malfunctions.

Explanation

Anomaly detection examines data to find instances that differ from established or expected patterns. When such deviations are discovered, they are typically flagged so that investigators or automated systems can determine whether the anomalies represent legitimate activity or problems that require mitigation.

Examples

Financial industry (fraud detection)

A credit card company may use anomaly detection to identify transactions that deviate from a customer’s usual spending habits, such as a sudden large purchase or a series of small transactions in a short period of time. These transactions can be flagged for further investigation to determine if they are legitimate or fraudulent.

Network security

A network administrator may use anomaly detection to monitor network traffic for unexpected spikes or patterns that could indicate a potential intrusion. If an anomalous pattern is detected, the administrator can take action to investigate and prevent the security breach.

Manufacturing

A manufacturing company may use anomaly detection to monitor the output of their production line for deviations from the expected range of values. If an anomalous output is detected, it can be flagged for further investigation to determine the cause and prevent future defects.

Use cases

• Fraud detection in financial transactions
• Identifying potential security breaches in network traffic
• Detecting defective products or equipment malfunctions in manufacturing

Related terms

• Fraud
• Security breach
• Technical malfunction