To identify patterns and relationships in data.

To detect and flag unusual or unexpected data points.

To predict future trends and outcomes.

To optimize model performance and accuracy.

K-Nearest Neighbors (KNN)

Principal Component Analysis (PCA)

Z-score

Support Vector Machines (SVM)

To find a hyperplane that separates normal data points from anomalies.

To cluster normal data points and identify anomalies as outliers.

To use a kernel function to transform data into a higher-dimensional space for anomaly detection.

To train a classifier using labeled data to distinguish between normal and anomalous data points.

Isolation Forest

Local Outlier Factor (LOF)

Gaussian Mixture Models (GMM)

Autoencoders

It isolates anomalies by randomly selecting features and splitting the data into smaller subsets.

It uses a decision tree ensemble to identify anomalies based on the depth of the tree required to isolate them.

It constructs a k-dimensional sphere around each data point and measures the anomaly score based on the number of points inside the sphere.

It trains a neural network to classify data points as normal or anomalous.

To measure the similarity between the input data and its reconstructed representation.

To identify anomalies as data points with high reconstruction errors.

To train the autoencoder to learn the normal data distribution and detect anomalies as deviations from this distribution.

To optimize the autoencoder's weights and biases to minimize the reconstruction error.

Accuracy

Precision

Recall

F1-score

It calculates the local density of each data point and identifies anomalies as points with low local density.

It measures the distance between each data point and its k-nearest neighbors to identify anomalies.

It constructs a k-dimensional sphere around each data point and measures the anomaly score based on the number of points inside the sphere.

It uses a decision tree ensemble to identify anomalies based on the depth of the tree required to isolate them.

The lack of labeled data for training supervised anomaly detection algorithms.

The high computational cost of some anomaly detection algorithms.

The difficulty in defining a clear boundary between normal and anomalous data points.

The presence of concept drift, where the underlying data distribution changes over time.

Isolation Forest

Local Outlier Factor (LOF)

Gaussian Mixture Models (GMM)

Self-Organizing Maps (SOM)

To identify malicious activities and attacks on computer systems and networks.

To detect and prevent unauthorized access to sensitive data and resources.

To monitor system performance and resource utilization for potential security breaches.

To analyze network traffic patterns to identify suspicious or anomalous behavior.

Isolation Forest

Local Outlier Factor (LOF)

Gaussian Mixture Models (GMM)

Change Point Detection (CPD)

The high dimensionality of time series data.

The presence of noise and outliers in time series data.

The difficulty in defining a clear boundary between normal and anomalous time series patterns.

The computational cost of anomaly detection algorithms for time series data.

Isolation Forest

Local Outlier Factor (LOF)

Gaussian Mixture Models (GMM)

Clustering-Based Anomaly Detection (CBAD)

The ability to learn complex and non-linear relationships in data.

The ability to handle high-dimensional data.

The ability to detect anomalies in real-time.

The ability to provide interpretable explanations for anomalies.