Anomáliaészlelés

Anomáliaészlelés, also known as anomaly detection or outlier detection, is a process of identifying patterns in data that do not conform to expected behavior. These non-conforming patterns are called anomalies or outliers. Anomaly detection is crucial in various fields for tasks such as fraud detection, intrusion detection in cybersecurity, fault detection in industrial systems, and medical diagnosis. The core challenge lies in the fact that anomalies are often rare and do not follow a predictable pattern, making their identification difficult.

Various techniques are employed for anomaly detection, broadly categorized into statistical methods, machine learning approaches, and rule-based systems. Statistical methods often assume a specific distribution for the data and identify points that deviate significantly from this distribution. Machine learning techniques, such as clustering, classification, and deep learning, can learn complex patterns from data and identify deviations without explicit prior assumptions about data distribution. Rule-based systems rely on predefined rules or expert knowledge to flag anomalies. The choice of technique depends heavily on the nature of the data, the type of anomalies expected, and the available computational resources. Evaluating the effectiveness of anomaly detection systems often involves metrics like precision, recall, and F1-score, considering the imbalanced nature of anomaly datasets.