Download An Introduction to Artificial Immune Systems

An Introduction to Artificial
Immune Systems
ES2001
Cambridge. December 2001.
Dr. Jonathan Timmis
Computing Laboratory
University of Kent at Canterbury
CT2 7NF. UK.
[email protected]
http:/www.cs.ukc.ac.uk/people/staff/jt6
Overview of Tutorial
What are we going to do?:
First Half:
Describe what is an AIS
Why bother with the immune system?
Be familiar with relevant immunology
Second Half:
Appreciation of were AIS are used
Be familiar with the building blocks of AIS
Resources
Immune metaphors
Other areas
Idea!
Idea ‘
Immune System Artificial Immune
Systems
Why the Immune System?
Recognition
Anomaly detection
Noise tolerance
Robustness
Feature extraction
Diversity
Reinforcement learning
Memory
Distributed
Multi-layered
Adaptive
Artificial Immune Systems
AIS are computational systems inspired by
theoretical immunology and observed
immune functions, principles and models,
which are applied to complex problem
domains (de Castro & Timmis, 2001)
Some History
Developed from the field of theoretical
immunology in the mid 1980’s.
Suggested we ‘might look’ at the IS
1990 – Bersini first use of immune algos to
solve problems
Forrest et al – Computer Security mid
1990’s
Hunt et al, mid 1990’s – Machine learning
Scope of AIS
Fault and anomaly detection
Data Mining (machine learning, Pattern
recognition)
Agent based systems
Scheduling
Autonomous control
Optimisation
Robotics
Security of information systems
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?
Primary lymphoid
organs
Secondary lymphoid
organs
Tonsils and
adenoids
Thymus
Spleen
Peyer’s patches
Appendix
Bone marrow
Lymph nodes
Lymphatic vessels
Multiple layers of the immune
system
Pathogens
Skin
Biochemical
barriers
Phagocyte
Innate
immune
response
Lymphocytes
Adaptive
immune
response
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
Antibodies
V
...
V
D
D
J
J
...
...
Antigen binding sites
VH
VL
VL
CH
Fab
Gene rearrangement
VH
D
J
C
Rearranged DNA
CH
CL
CL
V
Fab
Transcription
V
D
J
C
RNA
Splicing
CH
CH
V
Fc
D
J
C
mRNA
Translation
Heavy chain of an immunoglobulin
Antibody Molecule
Antibody Production
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)
Main Properties of Clonal
Selection (Burnet, 1978)
Elimination of self antigens
Proliferation and differentiation on contact of mature
lymphocytes with antigen
Restriction of one pattern to one differentiated cell and
retention of that pattern by clonal descendants;
Generation of new random genetic changes,
subsequently expressed as diverse antibody patterns by
a form of accelerated somatic mutation
T-cells
Regulation of other cells
Active in the immune response
Helper T-cells
Killer T-cells
TCR
T-cell
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)
Antibody Concentration
Cross-Reactive
Response
Secondary Response
Primary Response
Lag
Lag
Response
to Ag1
Lag
Response
to Ag1
...
...
Antigen Ag1
Antigens
Ag1, Ag2
...
Response to
Ag1 + Ag3
Response
to Ag2
...
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
Immune Network Theory(2)
Shape Space Formalism
Repertoire of the
immune system is
complete (Perelson, 1989)
Extensive regions of
complementarity
Some threshold of
recognition
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
Summary so far ….
Immune system has some remarkable
properties
Pattern recognition
Learning
Memory
So, is it useful?
Some questions for you !
Part II –Artificial Immune
Systems
This Section
General Framework for describing and
constructing AIS
A short review of where AIS are used today
Can not cover them all, far too many
I am not an expert in all areas (earn more
money if I was)
Where are AIS headed?
What do want from a Framework?
In a computational world we work with
representations and processes. Therefore,
we need:
To be able to describe immune system
components
Be able to describe their interactions
Quite high level abstractions
Capture general purpose processes that can be
applied to various areas
AIS Framework
De Castro & Timmis, 2002
Immune Representations
Immune Algorithms
Guidelines for developing AIS
Representation – Shape Space
Describe the general shape of a molecule
•Describe interactions between molecules
•Degree of binding between molecules
•Complement threshold
Representation
Vectors
Ab = Ab1, Ab2, ..., AbL
Ag = Ag1, Ag2, ..., AgL
Real-valued shape-space
Integer shape-space
Hamming shape-space
Symbolic shape-space
Define their Interaction
Define the term Affinity
Affinity is related to distance
Euclidian
D
L
2
(
Ab

Ag
)
 i
i
i 1
• Other distance measures such as Hamming,
Manhattan etc. etc.
• 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
A1 A2 A3 A4 A5 A6 A7 A8
A3
Library 2
Library 3
B1 B2 B3 B4 B5 B6 B7 B8
Library 4
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
Expressed Ab molecule
= four 16 bit segments
= a 64 bit chain
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
Detector Set
(R)
Self
strings (S)
Generate
random strings
(R0)
Match
No
Yes
Reject
Detector
Set (R)
Protected
Strings (S)
Match
Yes
Non-self
Detected
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)
Clonal Selection Algorithm
de Castro & von Zuben, 2001
Randomly initialise a population (P)
For each pattern in Ag
Determine affinity to each P’
Select n highest affinity from P
Clone and mutate prop. to affinity with Ag
Add new mutants to P
endFor
Select highest affinity P to form part of M
Replace n number of random new ones
Until stopping criteria
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
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
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
D*
0.6
0.7
0.8
0.9
1
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
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
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
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 (2001). Hybrid approach of
clonal selection and negative selection.
Forrests Model
External
host
Host
ip: 20.20.15.7
port: 22
sensitivity
level
Detector
set
Randomly created
010011100010.....001101
Immature
Datapath triple
(20.20.15.7, 31.14.22.87, ftp)
Internal
host
secondary
representation
Mature & Naive
ip: 31.14.22.87
port: 2000
Detector
0100111010101000110......101010010
Broadcast LAN
No match during
tolerization
state
Activation Last matches
flag
activated
Match
during
tolerization
Don’t
exceed
activation
threshold
Death
Exceed
activation
threshold
Match
Activated
No
co stimulation
Co stimulation
Memory
{immature, naive, memory}
AIS for computer network security. (a) Architecture. (b) Life cycle of a detec
Novelty Detection
Image Segmentation :
McCoy & Devarajan
(1997)
Detecting road
contours in aerial
images
Used a negative
selection algorithm
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
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)
Immune System : AIS
B-cell
B-cell recognition
Immune Network
Somatic
Hypermutation
Antigens
Antigen binding
Initial Data
Artificial Recognition
Ball
ARB Network
Mutation of ARB’s
Training data
Matching between
antigen and ARB’s
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
Watkins, 2001
Resource allocated mechanism (based on network
models)
Good classification rates on sample data sets
Robotics
Garbage
Behaviour Arbitration
Ishiguro et al. (1996, 1997)
: Immune network theory to
evolve a behaviour among
a set of agents
Middle
Far
Near
Robot
Collective Behaviour
Emerging collective
behaviour through
communicating robots (Jun
et al, 1999)
Immune network theory to
suppress or encourage
robots behaviour
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.
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.
Comparing Approaches
AIS
ANN
EA
Components
Attribute string in S
Artificial neurons
Location of components
Dynamic locations
Pre-defined/dynamic
(deterministic) locations
Structure
Set of discrete or networked
elements
Attribute strings/ network
connections
Learning/evolution
Networked neurons
Discrete elements
Connection strengths
Chromosomal strings
Learning
Evolution
Metadynamics
Elimination/recruitment of
components
Constructive/pruning algorithms
Elimination/ recruitment of
individuals
Interaction with other
components
Through recognition of attribute
strings or network connections
Through network connections
Interaction with the
environment
Recognition of an input pattern
or evaluation of an objective
function
Influences the affinity of
Input units receive the
environmental stimuli
Through recombination
operators and/or fitness
function
Evaluation of an objective
function
Knowledge storage
Dynamics
Threshold
Robustness
State
Control
Generalization
capability
Non-linearity
Characterization
elements
Population/network of
individuals
Concentration and affinity
Immune principle, theory or
process
Cross-reaction
Influences neuron activation
Strings representing
chromosomes
Dynamic locations
Network of individuals
Influences genetic
variations
Population of individuals
Activation level of output
neurons
Learning algorithm
Genetic information in
chromosomes
Evolutionary algorithm
Network extrapolation
Detection of common
schemas
Not explicit
Binding activation function
Neuronal activation function
Evolutionary and/or
connectionist
According to the learning
algorithm
Evolutionary
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
Framework helps a little
More formal approach required?
Wide possible application domains
What is it that makes the immune system
unique?
More Information
http://www.cs.ukc.ac.uk/people/staff/jt6
http://www.msci.memphis.edu/~dasgupta/
http://www.dcs.kcl.ac.uk/staff/jungwon/
http://www.dca.fee.unicamp.br/~lnunes/
http://www.cs.unm.edu/~forrest/