1. An Introduction to Artificial
Immune Systems
ES2001
Cambridge. December 2001.
Dr. Jonathan Timmis
Computing Laboratory
University of Kent at Canterbury
CT2 7NF. UK.
J.Timmis@ukc.ac.uk
http:/www.cs.ukc.ac.uk/people/staff/jt6

2. 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

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

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

5. 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)

6. 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

7. 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

8. Part I – Basic Immunology

9. 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

10. How does it work?

11. Where is it?

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

13. 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

14. Antibodies
Antigen binding sites
VH VH
VL VL
CH CH
Fab CL CL Fab
CH CH
Fc
Antibody Molecule Antibody Production

15. Clonal Selection

16. 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

17. T-cells
Regulation of other cells
Active in the immune response
Helper T-cells
Killer T-cells

18. 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

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

20. Immune Network Theory
Idiotypic network (Jerne, 1974)
B cells co-stimulate each other
Treat each other a bit like antigens
Creates an immunological memory

21. Immune Network Theory(2)

22. Shape Space Formalism
Repertoire of the V
×
immune system is Vε
ε
Vε
ε
complete (Perelson, 1989) × ×
×
Extensive regions of Vε
×
complementarity ε
×
×
Some threshold of
recognition

23. 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

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

25. Some questions for you !

26. Part II –Artificial Immune
Systems

27. 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?

28. 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

29. AIS Framework
De Castro & Timmis, 2002
Immune Representations
Immune Algorithms
Guidelines for developing AIS

30. Representation – Shape Space
Describe the general shape of a molecule
•Describe interactions between molecules
•Degree of binding between molecules
•Complement threshold

31. Representation
Vectors
Ab = 〈Ab1, Ab2, ..., AbL〉
Ag = 〈Ag1, Ag2, ..., AgL〉
Real-valued shape-space
Integer shape-space
Hamming shape-space
Symbolic shape-space

32. Define their Interaction
Define the term Affinity
Affinity is related to distance
L
Euclidian
D= ∑ ( Abi − Ag i ) 2
i =1
• Other distance measures such as Hamming,
Manhattan etc. etc.
• Affinity Threshold

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

34. 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

35. 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

36. 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)

37. 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

38. 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

39. 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

40. 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 α,

41. Part III - Applications

42. 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

43. 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 Algorithmic
code/data Virus Analysis
Extract Signature(s)
Add removal info
to database
Add signature(s) to databases

44. 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

45. 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

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

47. 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

48. 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.

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

50. Novelty Detection
Image Segmentation :
McCoy & Devarajan
(1997)
Detecting road
contours in aerial
images
Used a negative
selection algorithm

51. 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

52. 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

53. 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

54. Results (Timmis, 2000)

55. Immune System : AIS
B-cell Initial Data
B-cell recognition Artificial Recognition
Ball
ARB Network
Immune Network
Mutation of ARB’s
Somatic
Hypermutation
Training data
Antigens Matching between
Antigen binding antigen and ARB’s

56. 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

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

58. 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

59. 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) Paratope Idiotope
Desirable Interacting antibodies
Immune network theory to condition
Action
and degree of interaction
suppress or encourage
robots behaviour

60. 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.

61. 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.

62. Comparing Approaches
AIS ANN EA
Components Attribute string in S Artificial neurons Strings representing
chromosomes
Location of components Dynamic locations Pre-defined/dynamic Dynamic locations
(deterministic) locations
Structure Set of discrete or networked Networked neurons Discrete elements
elements
Knowledge storage Attribute strings/ network Connection strengths Chromosomal strings
connections
Dynamics Learning/evolution Learning Evolution
Metadynamics Elimination/recruitment of Constructive/pruning Elimination/ recruitment of
components algorithms individuals
Interaction with other Through recognition of attribute Through network connections Through recombination
components strings or network connections operators and/or fitness
function
Interaction with the Recognition of an input pattern Input units receive the Evaluation of an objective
environment or evaluation of an objective environmental stimuli function
Threshold function the affinity of
Influences Influences neuron activation Influences genetic
elements variations
Robustness Population/network of Network of individuals Population of individuals
individuals
State Concentration and affinity Activation level of output Genetic information in
neurons chromosomes
Control Immune principle, theory or Learning algorithm Evolutionary algorithm
process
Generalization Cross-reaction Network extrapolation Detection of common
capability schemas
Non-linearity Binding activation function Neuronal activation function Not explicit
Characterization Evolutionary and/or According to the learning Evolutionary
connectionist algorithm

63. 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

64. 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?

65. 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/