An Introduction to Artificial Immune Systems

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_at_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
Artificial Immune Systems
Immune System
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 1980s.
• Suggested we might look at the IS
• 1990 Bersini first use of immune algos to solve
  problems
• Forrest et al Computer Security mid 1990s
• Hunt et al, mid 1990s 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
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
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)
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 immune system is complete
  (Perelson, 1989)
• Extensive regions of complementarity
• Some threshold of recognition

V

V
e
e
V
e
e




V
e
e


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
• Euclidian

• 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 Dhaeseleer 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 ?,

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

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

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
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
Randomly
created
Host

ip 20.20.15.7
010011100010.....001101
Activation

port 22
Detector
threshold
set
Immature
Datapath
triple
Cytokine
level
No
match
during
(20.20.15.7, 31.14.22.87,
Internal
tolerization
ftp)
host
Permutation
mask
Exceed
Mature
Naive
activation
ip 31.14.22.87
threshold
Match
port 2000
Dont
during
Match
exceed
tolerization
Detector
Activated
activation
threshold
0100111010101000110......101010010
No
Co stimulation
co stimulation
memory

immature
activated
matches
Death
Memory
Broadcast LAN
  AIS for computer network security. (a)
Architecture. (b) Life cycle of a detector.
50
Novelty Detection

• Image Segmentation McCoy Devarajan (1997)
• Detecting road contours in aerial images
• Used a negative selection algorithm

51
Hardware Fault Tolerance
Table 4.1.           

• Immunotronics (Bradley Tyrell, 2000)
• Use negative selection algorithm for fault
  tolerance in 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
• B-cell recognition
• Immune Network
• Somatic Hypermutation
• Antigens
• Antigen binding

• Initial Data
• Artificial Recognition Ball
• ARB Network
• Mutation of ARBs
• Training data
• Matching between antigen and ARBs

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)
• Immune network theory to 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
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/