Anomaly Detection

By Prof. Seungchul Lee
http://iailab.kaist.ac.kr/
Industrial AI Lab at KAIST

Table of Contents

1. Anomaly

• Anomalies and outliers are essentially the same thing
  • Objects that are different from most other objects
  • Something that deviates from what is standard, or expected (one classification)

Causes of Anomalies

• Data from different class of object or underlying mechanism

  • disease vs. non-disease
  • fraud vs. not fraud

• Data measurement and collection errors

• Natural variation

  • tails on a Gaussian distribution

$$P_Y(Y=y) = \frac{1}{\sqrt{2\pi}} \exp \left(-\frac{1}{2} y^2 \right)$$

Anomaly Detection

• Finding outliers

Applications of Anomaly Detection

• Security & Surveillance

• Biomedical Applications

• Industrial Damage Detection

• Machinery Defects Diagnostics
  • Diagnosis of machinery conditions
  • Early alarm of malfunctioning

Difficulties with Anomaly Detection

• Scarcity of Anomalies

  • It is not easy to get anomaly data, because anomaly rarely happens
  • Overfitting issue occurs when there is only small number of data

• Diverse Types of Anomalies

  • There are so many causes of anomalies
  • At the training stage of neural network, we cannot have all possible anomalies as input data