Anomaly Detection
Anomaly Detection
Anomaly detection is a technique used in data mining and statistics to identify patterns or instances that deviate significantly from the normal behavior or expected pattern.
Since anomalies often indicate rare events or abnormalities, anomaly detection plays a crucial role in several fields.
Applications of Anomaly Detection
- Cybersecurity: Anomaly detection helps identify unusual activities or potential threats in network traffic, aiding in the prevention of cybersecurity breaches.
- Finance: Anomaly detection algorithms are used to detect fraudulent transactions, unusual market behavior, or anomalies in financial data.
- Healthcare: Anomaly detection can be applied to patient monitoring data to identify abnormal symptoms or potential diseases.
Popular Anomaly Detection Techniques
- Statistical methods: These techniques analyze data based on statistical parameters such as mean, standard deviation, and variance to identify deviations from normal behavior.
- Machine learning: Machine learning algorithms, such as clustering, classification, and neural networks, can be trained to detect anomalies based on pattern recognition.
- Time series analysis: Anomaly detection in time series data involves analyzing historical patterns and identifying deviations from expected trends or seasonal variations.
