Download Artificial Immune Systems: An Emerging Technology

Artificial Immune Systems: An
Emerging Technology
Congress on Evolutionary Computation 2001.
Seoul, Korea.
Dr. Jonathan Timmis
Computing Laboratory
University of Kent at Canterbury
England. UK.
[email protected]
http:/www.cs.ukc.ac.uk/people/staff/jt6
Tutorial Overview
What are Artificial Immune Systems?
Background immunology
Why use the immune system as a metaphor
Immune Metaphors employed
Review of AIS work
Applications
More blue sky research
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Immune metaphors
Other areas
Idea!
Idea ‘
Immune System Artificial Immune
Systems
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Artificial Immune Systems
Relatively new branch of computer science
Some history
Using natural immune system as a metaphor for
solving computational problems
Not modelling the immune system
Variety of applications so far …
Fault diagnosis (Ishida)
Computer security (Forrest, Kim)
Novelty detection (Dasgupta)
Robot behaviour (Lee)
Machine learning (Hunt, Timmis, de Castro)
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Why the Immune System?
Recognition
Anomaly detection
Noise tolerance
Robustness
Feature extraction
Diversity
Reinforcement learning
Memory
Distributed
Multi-layered
Adaptive
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Part I – 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 response
Remember past encounters
Faster response the second time around
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How does it work?
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Where is it?
Secondary lymphoid
organs
Primary lymphoid
organs
Tonsils and
adenoids
Thymus
Spleen
Peyer’s patches
Appendix
Lymph nodes
Bone marrow
Lymphatic vessels
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Multiple layers of the immune
system
Pathogens
Skin
Biochemical
barriers
Phagocyte
Innate
immune
response
Lymphocytes
Adaptive
immune
response
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Immune Pattern Recognition
BCR or Antibody
B-cell Receptors (Ab)
Epitopes
Antigen
B-cell
The immune recognition is based on the complementarity
between the binding region of the receptor and a portion of
the antigen called epitope.
Antibodies present a single type of receptor, antigens
might present several epitopes.
This means that different antibodies can recognize a single antigen
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Antibodies
V
...
V
D
D
J
J
...
...
Antigen binding sites
VH
V
VL
VL
CH
Fab
Gene rearrangement
VH
J
C
Rearranged DNA
CH
Fab
CL
CL
D
Transcription
V
D
J
C
RNA
Splicing
CH
CH
V
D
J
C
mRNA
Fc
Translation
Heavy chain of an immunoglobulin
Antibody Molecule
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Antibody Production
Artificial Immune Systems
C
Clonal Selection
Clonal deletion
(negative selection)
Self-antigen
Proliferation
(Cloning)
M
M
Antibody
Memory cells
Selection
Differentiation
Plasma cells
Foreign antigens
Self-antigen
Clonal deletion
(negative selection)
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T-cells
Regulation of other cells
Active in the immune response
Helper T-cells
Killer T-cells
TCR
T-cell
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Main Properties of Clonal
Selection (Burnet, 1978)
Elimination of self antigens
Proliferation and differentiation on contact of matu
lymphocytes with antigen
Restriction of one pattern to one differentiated cell an
retention of that pattern by clonal descendants;
Generation of new random genetic change
subsequently expressed as diverse antibody patterns b
a form of accelerated somatic mutation
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Reinforcement Learning and
Immune Memory
Repeated exposure to an antigen throughout
a lifetime
Primary, secondary immune responses
Remembers encounters
No need to start from scratch
Memory cells
Associative memory
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Learning (2)
Antibody Concentration
Cross-Reactive
Response
Secondary Response
Primary Response
Lag
Lag
Response
to Ag1
Lag
Response
to Ag1
...
...
Antigen Ag1
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...
Response to
Ag1 + Ag3
Response
to Ag2
Antigens
Ag1, Ag2
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...
Antigen
Ag1 + Ag3
Time
Immune Network Theory
Idiotypic network (Jerne, 1974)
B cells co-stimulate each other
Treat each other a bit like antigens
Creates an immunological memory
Suppression
Negative response
Paratope
Ag
1
2
Idiotope
3
Antibody
Activation
Positive response
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Immune Network Theory(2)
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Shape Space Formalism
Repertoire of the
immune system is
complete (Perelson, 1989)
Extensive regions of
complementarity
Some threshold of
recognition
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Ve
e
V

Ve


e


Ve
e


Self/Non-Self Recognition
Immune system needs to be able to
differentiate between self and non-self cells
Antigenic encounters may result in cell
death, therefore
Some kind of positive selection
Some element of negative selection
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Summary so far ….
Immune system has some remarkable
properties
Pattern recognition
Learning
Memory
So, is it useful?
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Some questions for you !
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Part II – A Review of Artificial
Immune Systems
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Topics to Cover
A few disclaimers …
I can not cover everything as there is a large
amount of work out there
To do so, would be silly 
Proposed general frameworks
Give an overview of significant application
areas and work therein
I am not an expert in all the problem domains
• I would earn more money if I was !
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Shape Space
Describe interactions between molecules
Degree of binding between molecules
Complement threshold
Each paratope matches a certain region of
space
Complete repertoire
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Representation and Affinities
Representation affects affinity measure
Binary
Integer
Affinity is related to distance
Euclidian
Hamming
Affinity threshold
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Basic Immune Models and
Algorithms
Bone Marrow Models
Negative Selection Algorithms
Clonal Selection Algorithm
Somatic Hypermutation
Immune Network Models
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Bone Marrow Models
Gene libraries are used to create antibodies from
the bone marrow
Antibody production through a random
concatenation from gene libraries
Simple or complex libraries
An individual genome corresponds to four libraries:
Library 1
A1 A2 A3 A4 A5 A6 A7 A8
A3
Library 2
Library 3
B1 B2 B3 B4 B5 B6 B7 B8
C1 C2 C3 C4 C5 C6 C7 C8
B2
D1 D2 D3 D4 D5 D6 D7 D8
C8
A3
B2
C8
D5
A3 B2 C8 D5
= four 16 bit segments
= a 64 bit chain
Expressed Ab molecule
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Library 4
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D5
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
Self
strings (S)
Generate
random strings
(R0)
Detector Set
(R)
Match
No
Detector
Set (R)
Protected
Strings (S)
Yes
Yes
Non-self
Detected
Reject
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Match
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No
Negative Selection Algorithm
Each copy of the algorithm is unique, so that each protected location is
provided with a unique set of detectors
Detection is probabilistic, as a consequence of using different sets of
detectors to protect each entity
A robust system should detect any foreign activity rather than looking
for specific known patterns of intrusion.
No prior knowledge of anomaly (non-self) is required
The size of the detector set does not necessarily increase with the
number of strings being protected
The detection probability increases exponentially with the number of
independent detection algorithms
There is an exponential cost to generate detectors with relation to the
number of strings being protected (self).
Solution to the above in D’haeseleer et al. (1996)
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Somatic Hypermutation
Mutation rate in proportion to affinity
Very controlled mutation in the natural immune
system
Trade-off between the normalized antibody
affinity D* and its mutation rate ,
1
0.9
0.8
0.7
 = 5
0.6

0.5
 = 10
0.4
 = 20
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
D*
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0.8
0.9
1
Immune Network Models
Timmis & Neal, 2000
Used immune network theory as a basis,
proposed the AINE algorithm
Initialize AIN
For each antigen
Present antigen to each ARB in the AIN
Calculate ARB stimulation level
Allocate B cells to ARBs, based on stimulation level
Remove weakest ARBs (ones that do not hold any B cells)
If termination condition met
exit
else
Clone and mutate remaining ARBs
Integrate new ARBs into AIN
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Immune Network Models
De Castro & Von Zuben (2000c)
aiNET, based in similar principles
At each iteration step do
For each antigen do
Determine affinity to all network cells
Select n highest affinity network cells
Clone these n selected cells
Increase the affinity of the cells to antigen by reducing the
distance between them (greedy search)
Calculate improved affinity of these n cells
Re-select a number of improved cells and place into matrix M
Remove cells from M whose affinity is below a set threshold
Calculate cell-cell affinity within the network
Remove cells from network whose affinity is below
a certain threshold
Concatenate original network and M to form new network
Determine whole network inter-cell affinities and remove all those
below the set threshold
Replace r% of worst individuals by novel randomly generated ones
Test stopping criterion
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Part III - Applications
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Anomaly Detection
The normal behavior of a system is often
characterized by a series of observations over
time.
The problem of detecting novelties, or anomalies,
can be viewed as finding deviations of a
characteristic property in the system.
For computer scientists, the identification of
computational viruses and network intrusions is
considered one of the most important anomaly
detection tasks
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Virus Detection
Protect the computer from unwanted viruses
Initial work by Kephart 1994
More of a computer immune system
Detect Anomaly
Scan for known viruses
Remove Virus
Capture samples using decoys
Send signals to
neighbor machines
Segregate
code/data
Algorithmic
Virus Analysis
Extract Signature(s)
Add removal info
to database
Add signature(s) to databases
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Virus Detection (2)
Okamoto & Ishida (1999a,b) proposed a
distributed approach
Detected viruses by matching self-information
first few bytes of the head of a file
the file size and path, etc.
against the current host files.
Viruses were neutralized by overwriting the selfinformation on the infected files
Recovering was attained by copying the same file
from other uninfected hosts through the computer
network
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Virus Detection (3)
Other key works include:
A distributed self adaptive architecture for a computer
virus immune system (Lamont, 200)
Use a set of co-operating agents to detect non-self
patterns
Immune System
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Computational System
Pathogens (antigens)
Computer viruses
B-, T-cells and antibodies
Detectors
Proteins
Strings
Antibody/antigen binding
Pattern matching
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Security
Somayaji et al. (1997) outlined mappings
between IS and computer systems
A security systems need
Confidentiality
Integrity
Availability
Accountability
Correctness
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IS to Security Systems
Immune System
Network Environment
Static Data
Self
Uncorrupted data
Non-self
Any change to self
Active Processes on Single Host
Cell
Active process in a computer
Multicellular organism
Computer running multiple processes
Population of organisms
Set of networked computers
Skin and innate immunity
Autoimmune response
Security mechanisms, like passwords, groups, file
permissions, etc.
Lymphocyte process able to query other processes to seek for
abnormal behaviors
False alarm
Self
Normal behavior
Non-self
Abnormal behavior
Adaptive immunity
Network of Mutually Trusting Computers
Organ in an animal
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Each computer in a network environment
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Network Security
Hofmeyr & Forrest (1999, 2000):
developing an artificial immune system that
is distributed, robust, dynamic, diverse and
adaptive, with applications to computer
network security.
Kim & Bentley (1999). New paper here at
CEC so I won’t cover it, go see it for
yourself!
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Forrests Model
External
host
ip: 20.20.15.7
port: 22
Randomly
created
Host
Activation Detector
threshold
set
010011100010.....001101
Immature
Datapath triple
Internal
host
Cytokine
level
No match during
tolerization
(20.20.15.7, 31.14.22.87,
ftp)
Permutation
mask
Mature & Naive
ip: 31.14.22.87
port: 2000
Detector
Match
during
tolerization
0100111010101000110......101010010
Broadcast LAN
immaturememory
activated matches
Don’t
exceed
activation
threshold
Death
Exceed
activation
threshold
Match
Activated
No
co stimulation
Co stimulation
Memory
AIS for computer network security. (a) Architecture. (b) Life cycle of a detec
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Novelty Detection
Image Segmentation :
McCoy & Devarajan
(1997)
Detecting road
contours in aerial
images
Used a negative
selection algorithm
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1.
2.
3.
4.
Generate random detectors
Apply these detectors to the sample data
Delete any detector misclassifying the sample data
Apply remaining detectors to the test image. Note pixels where a new
detector responds better than any previous detector
5. If enough pixels found improved detectors, go to Step 1
6. Output classified image
Artificial Immune Systems
Hardware Fault Tolerance
Immunotronics (Bradley & Tyrell, 2000)
Use negative selection algorithm for fault
tolerance in hardware
Immune System
Hardware Fault Tolerance
Recognition of self
Recognition of valid state/state transition
Recognition of non-self
Recognition of invalid state/state transition
Learning
Learning correct states and transitions
Humoral immunity
Error detection and recovery
Clonal deletion
Isolation of self-recognizing tolerance conditions
Inactivation of antigen
Return to normal operation
Life of an organism
Operation lifetime of a hardware
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Machine Learning
Early work on DNA Recognition
Cooke and Hunt, 1995
Use immune network theory
Evolve a structure to use for prediction of DNA
sequences
90% classification rate
Quite good at the time, but needed more
corroboration of results
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Unsupervised Learning
Timmis, 2000
Based on Hunts work
Complete redesign of algorithm: AINE
Immune metadynamics
Shape space
Few initial parameters
Stabilises to find a core pattern within a
network of B cells
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Results (Timmis, 2000)
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Another approach
de Castro and von Zuben, 2000
aiNET cf. SOFM
Use similar ideas to Timmis
• Immune network theory
• Shape space
Suppression mechanism different
• Eliminate self similar cells under a set threshold
Clone based on antigen match, network not
taken into account
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Results (de Castro & von Zuben,
2001)
Test Problem
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Result from aiNET
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Supervised Approach
Carter, 2000
Pattern recognition and classification system:
Immunos-81
Use T-cells, B-cells, antibodies and amino-acid
library
Builds a library of data types and classes
System can generalise
Good classification rates on sample data sets
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Robotics
Garbage
Behaviour Arbitration
Middle
Far
Near
Ishiguro et al. (1996, 1997)
: Immune network theory to
evolve a behaviour among
a set of agents
Collective Behaviour
Emerging collective
behaviour through
communicating robots (Jun
et al, 1999)
Immune network theory to
suppress or encourage
robots behaviour
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Robot
Battery charger
Garbage can
Paratope
Desirable
condition
Action
Idiotope
Interacting antibodies
and degree of interaction
Scheduling
Hart et al. (1998) and Hart & Ross (1999a)
Proposed an AIS to produce robust schedules
for a dynamic job-shop scheduling problem in which jobs arrive
continually, and the environment is subject to changes.
Investigated is an AIS could be evolved using a GA
approach
then be used to produce sets of schedules which together cover a
range of contingencies, predictable and unpredictable.
Model included evolution through gene libraries, affinity
maturation of the immune response and the clonal
selection principle.
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Diagnosis
Ishida (1993)
Immune network model applied to the process diagnosis
problem
Later was elaborated as a sensor network that could
diagnose sensor faults by evaluating reliability of data
from sensors, and process faults by evaluating reliability of
constraints among data.
Main immune features employed:
Recognition is performed by distributed agents which dynamically
interact with each other;
Each agent reacts based solely on its own knowledge; and
Memory is realized as stable equilibrium points of the dynamical
network.
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Summary
Covered much, but there is much work not
covered (so apologies to anyone for missing
theirs)
Immunology
Immune metaphors
Antibodies and their interactions
Immune learning and memory
Self/non-self
• Negative selection
Application of immune metaphors
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The Future
Rapidly growing field that I think is very
exciting
Much work is very diverse
Need of a general framework
Wide possible application domains
Lots of work to do …. Keep me in a job for
quite a while yet 
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