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Anomaly Detection Methods

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Neural Network-Based Anomaly Detection

Employs neural network architectures, such as autoencoders, that learn to reconstruct normal data. Anomalies are detected based on the reconstruction error. It's applied in various domains, including system health monitoring and detecting manufacturing defects.

Machine Learning-Based Anomaly Detection

Includes supervised, unsupervised, and semi-supervised learning models that are trained on data labeled as normal or anomalous. A common application is network intrusion detection. Unsupervised methods like clustering are often used when labels are not available.

Isolation Forest

An ensemble method that isolates anomalies by randomly selecting a feature and splitting the data. The anomalies are isolated closer to the root of the tree, as they are fewer than normal points. It is effective for high-dimensional datasets and is applied in security systems to detect intrusions.

Support Vector Machine-Based Anomaly Detection (One-Class SVM)

One-Class SVM is designed for anomaly detection in high-dimensional spaces. It learns a boundary around the normal data, and any data point outside this boundary is considered an anomaly. It's applied in applications like image processing for outlier detection.

Density-Based Anomaly Detection

This technique relies on the data distribution in a given feature space. Points in low-density regions are considered anomalies. The Local Outlier Factor (LOF) algorithm is a typical example of this method. It is frequently applied in fraud detection.

Statistical Anomaly Detection

This method assumes that the normal data follows a certain statistical distribution. Anomalies are detected by identifying the data points that deviate significantly from the statistical properties (e.g., mean, variance) of this distribution. Typical application is detecting credit card fraud.

Cluster Analysis-Based Anomaly Detection

This method clusters the data and detects anomalies as data points that do not belong to any cluster or are far away from their closest cluster. A typical application is customer segmentation in marketing to identify outliers in customer behavior.

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