Anomaly Detection in Security
Data Science
The identification of rare items events or observations that differ significantly from the majority of the data.
Detailed Explanation
Anomaly Detection in Data Science involves identifying unusual data points that deviate markedly from the majority of observations. These anomalies can indicate errors, fraud, network intrusions, or novel insights. The process uses statistical, machine learning, or clustering techniques to flag rare events, helping organizations monitor systems, ensure data integrity, and make informed decisions based on atypical patterns.
Use Cases
- •Detect fraudulent transactions in banking by flagging transactions that significantly differ from typical customer behavior.
Related Terms
Other terms in the Data Science category