1. Anomaly Detection
- S A L IL NAVG IR E

2. Introduction
• problem of finding patterns in data that do not
conform to expected behavior
• covers diverse disciplines from statistics, machine
learning, data mining, information theory, spectral
theory

3. Applications
• Intrusion detection- detection of malicious activity
• Host based – OS call traces
• Network based – packet level traces
• Fraud detection - detection of criminal activities in
commercial organizations
• Credit card fraud detection
• Insurance Claim Fraud Detection
• Insider trading detection
• Industrial damage detection
• Anomaly detection in data
• Anomaly detection in sensor networks

4. Challenges
• Defining normal region
• Sometimes malicious agent adapt themselves to
appear as normal observation
• Different techniques for different application
domain
• Availability of labeled data for training
• Sometimes noise is similar to anomaly and difficult
to distinguish

5. Different aspects of detection
techniques
• Nature of input data
• Types of Anomaly
• Point Anomalies
• Contextual Anomalies
• Collective Anomalies
• Data Labels
• Supervised anomaly detection
• Semi-Supervised anomaly detection
• Unsupervised anomaly detection
• Output
• Scores
• Labels

6. Anomaly Detection Techniques
Anomaly
detection
techniques
Classification
Nearest
Neighbor
Clustering
Spectral
Information
theoretic
Statistical
Time Series

7. • Classification
• Neural network based
• Bayesian Network based
• Support Vector Machine based
• Rule based
• Nearest Neighbor
• KNN
• Relative density
• Clustering
• K means
• SOM

8. • Statistical
• Parametric
• Gaussian model based
• Regression model based
• Mixture of parametric distributions based
• Non-parametric
• Histogram based
• Kernel function based
• Spectral
• Dimensionality reduction