V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science...
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![Page 1: V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science Research Day The University of Memphis March 25, 2005.](https://reader035.fdocuments.in/reader035/viewer/2022070305/55148543550346d36e8b4bcf/html5/thumbnails/1.jpg)
V-Detector: A Negative Selection AlgorithmZhou Ji, advised by Prof. Dasgupta
Computer Science Research Day The University of MemphisMarch 25, 2005
![Page 2: V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science Research Day The University of Memphis March 25, 2005.](https://reader035.fdocuments.in/reader035/viewer/2022070305/55148543550346d36e8b4bcf/html5/thumbnails/2.jpg)
Background Immune system
is a group of cells and organs that work together to fight infections in our bodies.
![Page 3: V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science Research Day The University of Memphis March 25, 2005.](https://reader035.fdocuments.in/reader035/viewer/2022070305/55148543550346d36e8b4bcf/html5/thumbnails/3.jpg)
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 ……
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Background
Different models of Artificial Immune Systems Negative selection algorithms Immune network model Clonal selection Gene library
![Page 5: V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science Research Day The University of Memphis March 25, 2005.](https://reader035.fdocuments.in/reader035/viewer/2022070305/55148543550346d36e8b4bcf/html5/thumbnails/5.jpg)
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
![Page 6: V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science Research Day The University of Memphis March 25, 2005.](https://reader035.fdocuments.in/reader035/viewer/2022070305/55148543550346d36e8b4bcf/html5/thumbnails/6.jpg)
Algorithm
basic idea
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Algorithm
V-detector
![Page 8: V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science Research Day The University of Memphis March 25, 2005.](https://reader035.fdocuments.in/reader035/viewer/2022070305/55148543550346d36e8b4bcf/html5/thumbnails/8.jpg)
Algorithm
V-detector’s features Simple generation strategy and
detector scheme - extensibility Variable sized detectors Coverage estimate Boundary-aware
![Page 9: V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science Research Day The University of Memphis March 25, 2005.](https://reader035.fdocuments.in/reader035/viewer/2022070305/55148543550346d36e8b4bcf/html5/thumbnails/9.jpg)
Implementation
Multiple dimensional, Real-valued representation
Control parameters Self threshold Target coverage Significant level (for hypothesis
testing) Boundary-aware vs. point-wise
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Implementation
User interface
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Experiments
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Summary
A new negative selection algorithm has been developed.
Important unique features. Challenges: evaluate the
detectors and categorize the anomaly.
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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