1 / 13

V-Detector: A Negative Selection Algorithm

Computer Science Research Day The University of Memphis March 25, 2005. V-Detector: A Negative Selection Algorithm. Zhou Ji, advised by Prof. Dasgupta. Background. Immune system is a group of cells and organs that work together to fight infections in our bodies. Background.

Download Presentation

V-Detector: A Negative Selection Algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related