cec2001-tutorial.ppt

Artificial Immune Systems: An
Emerging Technology
Dr. Jonathan Timmis
Computing Laboratory
University of Kent at Canterbury
England. UK.
J.Timmis@ukc.ac.uk
http:/www.cs.ukc.ac.uk/people/staff/jt6
Congress on Evolutionary Computation 2001.
Seoul, Korea.

CEC 2001 Artificial Immune Systems
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

Immune metaphors
Immune System
Idea! Idea ‘
Other areas
Artificial Immune
Systems

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)

Why the Immune System?
Recognition
Anomaly detection
Noise tolerance
Robustness
Feature extraction
Diversity
Reinforcement learning
Memory
Distributed
Multi-layered
Adaptive

Part I – Basic Immunology

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

How does it work?

Where is it?
Lymphatic vessels
Lymph nodes
Thymus
Spleen
Tonsils and
adenoids
Bone marrow
Appendix
Peyer’s patches
Primary lymphoid
organs
Secondary lymphoid
organs

Multiple layers of the immune
system
Phagocyte
Adaptive
immune
response
Lymphocytes
Innate
immune
response
Biochemical
barriers
Skin
Pathogens

Immune Pattern Recognition
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
B-cell
BCR or Antibody
Epitopes
B-cell Receptors (Ab)
Antigen

Antibodies
Antigen binding sites
VH
VL
CH CH
VL
CL
CH
VH
CH
CL
Fc
Fab
Fab
Antibody Molecule
... ... ...
V V
D D J J C
Gene rearrangement
Rearranged DNA
V D J C
Transcription
RNA
V D J C
Splicing
mRNA
V D J C
Translation
Heavy chain of an immunoglobulin
Antibody Production

Clonal Selection
Foreign antigens
Proliferation
(Cloning)
Differentiation
Plasma cells
Memory cells
Selection
M
M
Antibody
Self-antigen
Self-antigen
Clonal deletion
(negative selection)
Clonal deletion
(negative selection)

T-cells
Regulation of other cells
Active in the immune response
Helper T-cells
Killer T-cells
T-cell
TCR

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

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

Learning (2)
Antigen Ag1
Antigens
Ag1, Ag2
Primary Response Secondary Response
Lag
Response
to Ag1
Antibody
Concentration
Time
Lag
Response
to Ag2
Response
to Ag1
...
...
Cross-Reactive
Response
...
...
Antigen
Ag1 + Ag3
Response to
Ag1 + Ag3
Lag

Immune Network Theory
Idiotypic network (Jerne, 1974)
B cells co-stimulate each other
Treat each other a bit like antigens
Creates an immunological memory
1
2
3
Ag
Activation
Positive response
Suppression
Negative response
Antibody
Paratope
Idiotope

Immune Network Theory(2)

Shape Space Formalism
Repertoire of the
immune system is
complete (Perelson, 1989)
Extensive regions of
complementarity
Some threshold of
recognition
e
Ve
e
Ve
e
Ve
V








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

Summary so far ….
Immune system has some remarkable
properties
Pattern recognition
Learning
Memory
So, is it useful?

Some questions for you !

Part II – A Review of Artificial
Immune Systems

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 !

Shape Space
Describe interactions between molecules
Degree of binding between molecules
Complement threshold
Each paratope matches a certain region of
space
Complete repertoire

Representation and Affinities
Representation affects affinity measure
Binary
Integer
Affinity is related to distance
Euclidian
Hamming
Affinity threshold

Basic Immune Models and
Algorithms
Bone Marrow Models
Negative Selection Algorithms
Clonal Selection Algorithm
Somatic Hypermutation
Immune Network Models

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 Library 2 Library 3 Library 4
A1 A2 A3 A4 A5 A6 A7 A8
A3 D5
C8
B2
A3 D5
C8
B2
A3 B2 C8 D5
= four 16 bit segments
= a 64 bit chain
Expressed Ab molecule
B1 B2 B3 B4 B5 B6 B7 B8 C1 C2 C3 C4 C5 C6 C7 C8 D1 D2 D3 D4 D5 D6 D7 D8

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)
Match Detector
Set (R)
Reject
No
Yes
No
Yes
Detector Set
(R)
Protected
Strings (S)
Match
Non-self
Detected

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)

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 ,
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
D*

 = 5
 = 10
 = 20

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

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

Part III - Applications

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

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
Capture samples using decoys
Extract Signature(s)
Add signature(s) to databases
Add removal info
to database
Segregate
code/data
Algorithmic
Virus Analysis
Send signals to
neighbor machines
Remove Virus

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 self-
information on the infected files
Recovering was attained by copying the same file
from other uninfected hosts through the computer
network

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 Computational System
Pathogens (antigens) Computer viruses
B-, T-cells and antibodies Detectors
Proteins Strings
Antibody/antigen binding Pattern matching

Security
Somayaji et al. (1997) outlined mappings
between IS and computer systems
A security systems need
Confidentiality
Integrity
Availability
Accountability
Correctness

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 Security mechanisms, like passwords, groups, file
permissions, etc.
Adaptive immunity Lymphocyte process able to query other processes to seek for
abnormal behaviors
Autoimmune response False alarm
Self Normal behavior
Non-self Abnormal behavior
Network of Mutually Trusting Computers
Organ in an animal Each computer in a network environment

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!

Forrests Model
AIS for computer network security. (a) Architecture. (b) Life cycle of a detec
Datapath triple
(20.20.15.7, 31.14.22.87,
ftp)
Broadcast LAN
ip: 31.14.22.87
port: 2000
Internal
host
External
host
ip: 20.20.15.7
port: 22
Host
Activation
threshold
Cytokine
level
Permutation
mask
Detector
set
immature
memory activated matches
0100111010101000110......101010010
Detector
Randomly
created
Immature
Mature & Naive
Death
Activated
Memory
No match during
tolerization
010011100010.....001101
Exceed
activation
threshold
Don’t
exceed
activation
threshold
No
co stimulation
Co stimulation
Match
Match
during
tolerization

Novelty Detection
Image Segmentation :
McCoy & Devarajan
(1997)
Detecting road
contours in aerial
images
Used a negative
selection algorithm
1. Generate random detectors
2. Apply these detectors to the sample data
3. Delete any detector misclassifying the sample data
4. 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

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

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

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

Results (Timmis, 2000)

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

Results (de Castro & von Zuben,
2001)
Test Problem Result from aiNET

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

Robotics
Behaviour Arbitration
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
Garbage
Robot
Garbage can
Battery charger
Far
Middle
Near
Desirable Interacting antibodies
condition and degree of interaction
Action
Paratope Idiotope

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.

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.

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

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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