V-Detector: A Negative Selection AlgorithmZhou Ji, advised by Prof. Dasgupta

Computer Science Research Day The University of MemphisMarch 25, 2005

Background Immune system

is a group of cells and organs that work together to fight infections in our bodies.

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 ……

Background

Different models of Artificial Immune Systems Negative selection algorithms Immune network model Clonal selection Gene library

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

Algorithm

basic idea

Algorithm

V-detector

Algorithm

V-detector’s features Simple generation strategy and

detector scheme - extensibility Variable sized detectors Coverage estimate Boundary-aware

Implementation

Multiple dimensional, Real-valued representation

Control parameters Self threshold Target coverage Significant level (for hypothesis

testing) Boundary-aware vs. point-wise

Implementation

User interface

Experiments

Summary

A new negative selection algorithm has been developed.

Important unique features. Challenges: evaluate the

detectors and categorize the anomaly.

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