Anomaly Detection in Cybersecurity

What is Anomaly Detection?

Anomaly detection is the process of identifying data points, events, or observations that deviate significantly from the expected or normal pattern in a dataset. (Sozol et al., 2024)

In the context of cybersecurity, anomaly detection is used to identify unusual or suspicious activities that may indicate a security breach or cyber attack. This could include detecting unusual network traffic, unusual user behavior, or unusual system events that may be indicative of malicious activity.

Types of Anomalies

Point/Rare Anomalies

Point anomalies are individual data points that deviate significantly from the normal range of values in the dataset. These are often easy to detect as they are isolated from the bulk of the data. (Sozol et al., 2024)

Contextual Anomalies

Contextual anomalies are data points that are anomalous with respect to their surrounding context or environment. For example, a particular sensor reading that is much higher than other readings from the same sensor at a specific time period. (Sozol et al., 2024)

Anomaly Detection Techniques

Statistical Techniques

Traditional anomaly detection methods often relied on statistical techniques to identify outliers in data, such as:

• Clustering algorithms (e.g., k-means, DBSCAN) to group similar data points and identify outliers
• Density-based methods (e.g., Local Outlier Factor) to detect anomalies based on the density of data points
• Isolation Forest to isolate anomalies by randomly partitioning the feature space

Machine Learning Techniques

More advanced anomaly detection techniques leverage machine learning models, such as:

• Autoencoders to learn a compressed representation of normal data and identify anomalies as data points that cannot be well reconstructed
• Recurrent Neural Networks (e.g., LSTM, GRU) to capture temporal patterns and detect anomalies in time series data
• Ensemble methods that combine multiple anomaly detection algorithms for improved performance

Graph-based Techniques

Graph-based anomaly detection techniques leverage the relationships and connections between data points to identify anomalies. These methods are particularly useful for cybersecurity applications, where the data can be represented as a graph of entities (e.g., users, devices, network connections) and their interactions. (Sozol et al., 2024)

Some key graph-based techniques include:

• Graph Neural Networks (GNNs) to learn representations of the graph structure and identify anomalous patterns (Sozol et al., 2024)
• Behavioral Identification Graphs (BIGs) to model normal user and device behavior and detect deviations (Sozol et al., 2024)
• Graph-based Botnet Detection (GBBD) to identify coordinated malicious activities across a network (Sozol et al., 2024)