Anomáliafelismerés

Anomáliafelismerés, also known as anomaly detection or outlier detection, is a process of identifying patterns in data that do not conform to expected behavior. These unusual patterns are often referred to as anomalies, outliers, novelties, noise, deviations, or exceptions. The goal of anomaly detection is to find data points that are significantly different from the majority of the data.

This technique finds applications across various domains. In cybersecurity, it is used to detect malicious activities such as intrusions or fraud. In manufacturing, it can identify defective products on an assembly line. In healthcare, anomaly detection can flag unusual patient readings that might indicate a health issue. Financial institutions use it to detect fraudulent transactions.

There are several approaches to anomaly detection. Statistical methods assume that normal data follows a certain probability distribution, and deviations from this distribution are considered anomalies. Machine learning techniques are also widely employed. Supervised methods require labeled data (both normal and anomalous), while unsupervised methods work with unlabeled data, identifying outliers based on their distance from other data points or their lack of belonging to any cluster. Semi-supervised methods use only normal data for training, assuming any deviation from the learned normal pattern is an anomaly. The choice of method often depends on the nature of the data and the availability of labels.