Anomaly Detection

Anomaly detection, also known as outlier detection, is a technique in computer science used to identify data points, events, or observations that deviate significantly from a dataset's normal behavior. By establishing a baseline of normalcy, often through statistical analysis or machine learning algorithms, systems can automatically flag these unusual instances. This capability is a cornerstone of modern cybersecurity, where it is applied to detect network intrusions, fraudulent financial transactions, and other malicious activities that manifest as aberrations from established patterns of legitimate user or system activity.

1. Introduction to Anomaly Detection
   1. Defining Anomalies and Outliers
      1. Basic Definitions and Terminology
      2. Distinction between Anomalies and Noise
      3. Distinction between Anomalies and Outliers
      4. Mathematical Formulations of Anomalies
   2. Types of Outliers by Scope
      1. Global Outliers
      2. Local Outliers
      3. Contextual Outliers
   3. The Concept of Normal Behavior
      1. Statistical Definitions of Normality
      2. Domain-Specific Notions of Normality
      3. Baseline Modeling Approaches
      4. Dynamic vs Static Normal Behavior
   4. Importance and Applications of Anomaly Detection
      1. Security and Safety Applications
      2. Economic Impact and Business Value
      3. Data Quality Assurance
      4. Scientific Discovery and Research
   5. Classification of Anomaly Detection Methods
      1. Supervision-Based Classification
         1. Supervised Methods
         2. Semi-supervised Methods
         3. Unsupervised Methods
      2. Output-Based Classification
         1. Anomaly Scores
         2. Binary Classification
         3. Ranking-Based Outputs
      3. Technique-Based Classification
         1. Statistical Methods
         2. Machine Learning Methods
         3. Proximity-Based Methods
   6. Types of Anomalies by Structure
      1. Point Anomalies
         1. Individual Data Point Deviations
         2. Threshold-Based Detection
      2. Contextual Anomalies
         1. Temporal Context
         2. Spatial Context
         3. Behavioral Context
      3. Collective Anomalies
         1. Sequence Anomalies
         2. Group Anomalies
         3. Pattern-Based Anomalies