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

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 systems 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. Algorithm basic idea

7. Algorithm V-detector

8. Algorithm V-detectors 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. Implementation User interface

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