Artificial Immune Systems

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ARTIFICIAL IMMUNE SYSTEMS

BY:Nazanin AsadiZohre Molaei

Isfahan University of Technology

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Outline

History Natural Immune System Artificial Immune System Application Experiment Result Reference

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History

Developed from the field of theoretical immunology in the mid 1980’s.

1990 – Bersini first use of immune algorithms to solve problems

Forrest et al – Computer Security mid 1990’s

Hunt et al, mid 1990’s – Machine learning

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Basic Immunology

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Role of the Immune System

Protect our bodies from infection Primary immune response

Launch a response to invading pathogens

Secondary immune responseRemember past encountersFaster response the second time around

The IS is adaptable (presents learning and memory)

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Where is it ?

Lymphatic vessels

Lymph nodes

Thymus

Spleen

Tonsils andadenoids

Bone marrow

Appendix

Peyer’s patches

Primary lymphoidorgans

Secondary lymphoidorgans

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Lymphocytes

Carry antigen receptors that are specificThey are produced in the bone marrow through random re-arrangement

B and T Cells are the main actors of the adaptive immune system

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B Cell Pattern Recognition

B cells have receptors called antibodies

The immune recognition is based on the

complementarity between the binding

region of the receptor and a portion of the

antigen called the epitope.

Recognition is not just by a single

antibody, but a collection of them

Learn not through a single agent,

but multiple ones

B-cell

BCR or Antibody

Epitopes

B-cell Receptors (Ab)

Antigen

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T-cells

Regulation of other cells

Active in the immune response

Helper T-cells

Killer T-cells

T-cell

TCR

APC

MHC-II Protein Antigen

Peptide

TH cell

MHC/peptide complex

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Immune Responses

Antigen Ag1 Antigens Ag1, Ag2

Primary Response Secondary Response

Lag

Response to Ag1

Ant

ibod

y C

once

ntra

tion

Time

Lag

Response to Ag2

Response to Ag1

...

...

Cross-Reactive Response

...

...

Antigen Ag1 + Ag3

Response to Ag1 + Ag3

Lag

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The Immune System models•The are many different viewpoints•These views are not mutually exclusive

classical

networkdanger

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Artificial Immune Systems

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Basic concepts

trained detectors(artificial lymphocytes) that detect nonself

patterns

need a good repository of self patterns or self and non-self

patterns to train ALCs to be self tolerant

need to measure the affinity between an ALC and a pattern

To be able to measure affinity, the representation of the patterns

and the ALCs need to have the same structure

The affinity between two ALCs needs to be measured

memory that frequently detect non-self patterns

When an ALC detects non-self patterns, it can be cloned and the

clones can be mutated to have more diversity in the search

space

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AIS Framework

Algorithms

Affinity

Representation

Application

Solution

AIS

Shape-Space

Binary

Integer

Real-valued

Symbolic

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Representation – Shape Space

Used for modeling antibody and antigen

Determine a measure to calculate affinity

Hamming shape space(binary)

1 if Abi != Agi: 0 otherwise (XOR

operator)

Antibody

Antigen

0 0 1 1 0 0 1 1

1 1 1 0 1 1 0 1

Ab:

Ag:

1

0

1

0

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Representation

Assume the general case: Ab = Ab1, Ab2, ..., AbL Ag = Ag1, Ag2, ..., AgL

Binary representation

Matching by bits Continuous (numeric)

Real or Integer, typically Euclidian Symbolic (Categorical /nominal)

E.g female or male of the attribute Gender.

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AIS Framework

Algorithms

Affinity

Representation

Application

Solution

AISEuclidean

Manhattan

Hamming

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Affinity

Euclidean

Manhattan

Hamming

L

1i

2ii )Ag(AbD

L

iii AgAbD

1

L

i

ii AgAbD

1 otherwise0

if1δwhereδ,

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AIS Framework

Algorithms

Affinity

Representation

Application

Solution

AIS

Bone Marrow Models

Negative Selection

Clonal Selection

Positive Selection

Immune Network Models

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Basic AIS Algorithm

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Negative Selection Algorithms

Forrest 1994: Idea taken from the negative selection of T-cells in the thymus

Applied initially to computer security Split into two parts: Censoring Monitoring

Selfstrings (S)

Generaterandom strings

(R0)Match Detector

Set (R)

Reject

No

Yes

No

Yes

Detector Set(R)

ProtectedStrings (S)

Match

Non-selfDetected

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All patterns and ALCs : as nominal valued attributes or as binary strings

Affinity : r-continuous matching rule

Training set : self patterns

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Training ALCs with negative selection

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Clonal Selection

Antigens

Proliferation Differentiation

Plasma cells

Memory cells

Selection

M

M

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Clonal Selection

selection of a set of ALCs with the highest calculated affinity with a non-self pattern

cloned and mutated

compete with the existing set of ALCs

to be exposed to the next non-self pattern Continuous (numeric)

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ClONALG algorithm

De Castro and Von presented CLONALG as an algorithm,2001

initially proposed to perform machine-learning pattern recognition

Adapted to be applied to optimization problem

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ClONALG algorithm

main immune aspects taken into account to develop the algorithm maintenance of a specific memory set selection and cloning of the most stimulated

Antibodies death of non-stimulated antibodies affinity maturation and re-selection of the

clones proportionally to their antigenic affinity generation and maintenance of diversity

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ClONALG

All patterns in binary strings

Training set : non-self patterns

Affinity : Hamming distance , between ALC and

non-self pattern

Lower Hamming distance = stronger affinity

Assumption : One memory ALC for each of the

patterns that needs to be recognized in training

set

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ClONALG

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CLONALG optimization case

an objective function g(⋅) must to be optimized

(maximized or minimized)

antibody affinity corresponds to the objective

function

each antibody Abi represents an element of the

input space

it is no longer necessary to maintain a separate

memory set Ab

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Immune Network Models

The ALCs interact with each other to learn the

structure of a non-self pattern

The ALCs in a network co-stimulates and/or co-

suppress each other to adapt to the non-self pattern

The stimulation of an ALC based on the calculated affinity between the ALC

and the non-self pattern the calculated affinity between the ALC and

network ALCs as co-stimulation and/or co-suppression.

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Artificial Immune Network

Timmis and Neal,2000

Application clustering

data visualization

control

optimization domains

AINE defines the new concept of artificial recognition balls

(ARBs)

population of ARBs links between the ARBs a set of antigen training patterns Some clonal operations for learning

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all training patterns in set DT are presented to

the set of ARBs

After each iteration, each ARB calculates its stimulation level Allocates resources (i.e. B-Cells) based on its

stimulation level as

The stimulation level antigen stimulation network stimulation network suppression

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Stimulation level

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Resource allocation

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Danger Theory Models

distinguishes between

what is dangerous and

non-dangerous

Include a signal to

determine whether a

non-self pattern is

dangerous or not

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An Adaptive Mailbox

classifies interesting from uninteresting emails

initialization phase (training)

running phase (testing)

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initialization phase

running phase

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Application of AIS

network intrusion and anomaly detection

data classification models

virus detection

concept learning

data clustering

robotics

pattern recognition and data mining

optimization of multi-modal functions

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PSO and AIS

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PSO and AIS

PSO performs about 56 percent faster than.

AIS performs faster than PSO (about 14 percent)

for simpler mathematical functions

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Reference

Computational Intelligence An introduction ,

Adndries P.Engelbrecht

Learning and Optimization Using the Clonal

Selection Principle, Leandro N. de

Castro, ,Fernando J. Von Zuben, IEEE,2002

A Comparative Analysis on the Performance of

Particle Swarm Optimization and Artificial Immune

Systems for Mathematical Test Functions, 1David

F.W. Yap, 2S.P. Koh, 2S.K. Tiong,Australian Journal

of Basic and Applied Sciences, 2009

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Artificial Immune Systems