Chapter: Anomaly Detection
From Computing Project Repository
The "Anomaly Detection" chapter
- Introduction and Motivations:
--> what is anomaly detection and its applications --> confusions and misunderstanding --> misuse of the term
- Basic Anomaly Detection Techniques:
--> Statistic-based Approaches --> Specification-based Approaches --> False positives versus false negatives
- Profiling the Features:
--> Feature Selection --> The Machine Learning Approaches
- Performance Evaluation:
--> Analytical Approaches (e.g., Entropy-based Metrics) --> Data-centric Benchmarks and Experiments (e.g., Data set) --> Data Manipulation for Anomaly Detection
- Issues: The usefulness of Anomaly Detection
--> On Efficiently/Practically Building the Long-term Profiles --> Explanation of Detected Anomalies --> Responing the Anomalies
- Examples: the Systems
--> two or three examples such as (Self/Why 6), NIDES, Elisha.
- Summary and Future Research Topics