V-Detector: A Negative Selection Algorithm

1. Computer Science Research Day The University of Memphis March 25, 2005 V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta

2. Background • Immune system is a group of cells and organs that work together to fight infections in our bodies.

3. Background • AIS (Artificial Immune Systems) are not just intrusion detection and defense • Immune system’s computational capability • Learning • Memory • Recognition • Feature extraction • Distributed process • Adaptation • Self/nonself discrimination • Prediction • ……

4. Background • Different models of Artificial Immune Systems • Negative selection algorithms • Immune network model • Clonal selection • Gene library

5. Background • Negative Selection Algorithms • In natural immune system: T-cells develop in thymus • Random generation + aimed elimination • Represent target concept by negative space • Training only with self samples – “one class” learning

6. basic idea Algorithm

7. V-detector Algorithm

8. Algorithm • V-detector’s features • Simple generation strategy and detector scheme - extensibility • Variable sized detectors • Coverage estimate • Boundary-aware

9. Implementation • Multiple dimensional, Real-valued representation • Control parameters • Self threshold • Target coverage • Significant level (for hypothesis testing) • Boundary-aware vs. point-wise

10. User interface Implementation

11. Experiments

12. Summary • A new negative selection algorithm has been developed. • Important unique features. • Challenges: evaluate the detectors and categorize the anomaly.

13. Bibliography • Ji & Dasgupta, Augmented Negative Selection Algorithm with Variable-Coverage Detectors, CEC 2004 • Ji & Dasgupta, Real-valued Negative Selection Algorithm with Variable-Sized Detectors, GECCO 2004 • Ji & Dasgupta, Estimating the Detector Coverage in a Negative Selection Algorithm, GECCO 2005