Inspiration to Application: A Tutorial on Artificial Immune Systems

From Inspiration to Application:
Artiﬁcial Immune Systems

Dr. Julie Greensmith
Intelligent Modelling & Analysis
School of Computer Science
julie.greensmith@nottingham.ac.uk
Tuesday, 6 December 11

Overview

• Introductions: me, myself and AIS

• A short history of Artiﬁcial Immune Systems

• 1st Generation Algorithms

• Interdisciplinary development of immune-inspired algorithms

• Finding our identity and moving forward

• Computational Immunology

• Systems thinking in AIS


Introductions

• BSc in Pharmacology @ Uni. Leeds

• got addicted to programming, especially C

• MSc in Multidisciplinary Informatics @ U.Leeds

• PhD in Artiﬁcial Immune Systems @ Uni. Notts

• Anne McLaren Fellow @ Uni. Notts

• Lecturer in Computer Science

• Soprano Cornet in Ilkeston Brass Band


Research Interests

• Bioinspired computing and computational intelligence

• Performing inter- and multidisciplinary research

• computer security to immunology

• computational intelligence to adaptive entertainment systems

• Development of complex system analysis tools

• In-line with interests of my two research groups

• End-to-end modelling and analysis, mixed reality applications


A Short History Of Artiﬁcial Immune Systems


Why Be Bio-inspired?


A Very Natural Idea

• Informal interdisciplinary collaboration between a theoretical
immunologist and a computer scientist

• attempting to solve the same problem from different angles

• Inspiration from the human immune system

• robust, decentralised, error tolerant, homeostatic and adaptive

• Pattern recognition, anomaly detection, robotics, optimization


A Very Natural Idea

• Informal interdisciplinary collaboration between a theoretical
immunologist and a computer scientist

• attempting to solve the same problem from different angles

• Inspiration from the human immune system

• robust, decentralised, error tolerant, homeostatic and adaptive

• Pattern recognition, anomaly detection, robotics, optimization

Why not create a computer immune system
to combat computer viruses?

1st Generation Algorithms (1994-2003)

• Based on simple textbook models of immunology

• thymic selection of naive T-Cells

• antibody production and memory through B-Cells

• Based on basic theoretical or computational models of immune
function according to the principles of central tolerance

• No involvement of either practical or theoretical immunologists
past the ﬁrst initial informal collaboration

• based on computer science’s elementary understanding of
biology


Negative Selection


Negative Selection Applications

• Negative selection is an anomaly detection algorithm

• One-class learning for two-class classiﬁcation

• Vector representation of a dataset

• Computer security including detection of anomalous TCP packets
and sequences of system calls

• Fault detection in computational and mechanical systems including
aircraft and cash machines

• Ji & Dasgupta, Revisiting Negative Selection Algorithms (2007),
Vol. 15, No. 2, Pages 223-25


Negative Selection: Some Issues

• Originally conceptualised to detect computer network anomalies

• One shot learning algorithm

• cannot adapt to changes such as concept drift

• Generation of false positives

• lack of adaptivity and failure to provide adequate shape-space coverage
resulting in ‘holes’

• Issues with scaling

• as the number of dimensions increase or if real valued vectors are used,
the process becomes computationally infeasible


Clonal Selection

yes, its pretty much a GA without crossover

Clonal Selection Applications

• Adaptive optimisation based algorithm

• Applied to most of the standard optimisation and machine learning
problems including travelling salesman, knapsack problem,
combinatorial optimisation, and of course the iris dataset (frequently)

• Applications for many datasets with vector representation

• e.g Potato Seed Production, handwriting recognition, microwave
matching functions, circuit board design, print layout patterns,

• pattern recognition in image processing

• structural similarities in protein structure prediction


Immune Network and Idiotypic Approaches

• Based on an unproven theory of interaction (Varela, 1978)

• Similar to clonal selection except, suppression incorporated
into afﬁnity

• Antibodies interact via stimulation or suppression with other
antibodies in addition to antigens - cooperative behaviour

• Some success in mobile robotics and scheduling

• Spawned the general form of “immune network approaches”


Idiotypic Network for Robotics

• A. Whitbrook & U. Aickelin (2006-2008) in IMA


The Curse Of The ‘Ladybird’ Books

• These algorithms did not produce the expected performance in
numerous different terms

• Negative selection:
• scaling (Stibor et al, 2006), false positives (Kim & Bentley, 2001)
• Clonal Selection:
• analagous to a hybrid between k-means and a genetic algorithm
(Stibor et al, 2008)

• Idiotypic networks: produced adequate performance, issues of
scaling and coverage and they’re a bit tricky to tune

Erm, excuse me, thats not an immune system....


So what exactly is an AIS??

• Seemed to be no distinct advantage to using these algorithms

• Produce algorithms which are not easy to understand, or to
explain - not like a genetic algorithm

• An identity crisis: what deﬁned an AIS (Timmis & Decastro 2001)

• No “killer application” identiﬁed hence viewed as not really
useful

• Was bio-inspiration actually any good?

• As long as you match between two items in feature space, gave
researchers the licence to call it an AIS


Our Second Approach:
We Talked to Immunologists (2004-2010)


A Paradigm Shift

• To derive algorithms sufﬁciently complex, the underlying models
must also be sufﬁciently complex

• Inclusion of practical and theoretical immunologists in debate
over how to proceed

• Engagement with interdisciplinary computer scientists

• Collaboration with biological-based mathematicians

• Initially facilitated through the EPSRC funded ARTIST network
and an Adventure Grant

• Prof Jon Timmis (York), Prof Uwe Aickelin (Nottingham)


Two Approaches In Contrast

• There is currently no general consensus as to what is the
“best practice” method for interdisciplinary working in AIS

• Large distributed project team using a software engineering
inspired methodology based on iterative waterfall approach

• “The Conceptual Framework”

• Smaller close knit team of interdisciplinary people

• A combination of disciplinary and interdisciplinary research

• “The Danger Project” - I was the CS PhD student


Inspiration From The Danger Theory

• Previously thought to be ‘self-nonself discrimination’

• if your name’s not on the list, then you are eliminated

• The Danger Model

• immune system is activated if damage is detected

• immune system suppressed if tissues are healthy

• Decision made by Dendritic Cells (DCs) via tissue based
context detection and MHC-II based antigen presentation


Necrosis Versus Apoptosis


Why is this Computationally Interesting?

• The innate immune system can tell the difference between a tissue full
of necrotic product and a tissue containing apoptotic cells

• Necrosis results in the release of internal cell contents which degrade
and become danger signals (Olpan et al 2010).

• apoptotic context yields semi-mature cells

• necrotic context yields mature cells

• It is not the presence of antigen but the detection of damage which is
the initial activator of the human immune system

• Surely this is the most appropriate mechanism for computer security?


Collaboration with Immunologists

• The literature is not always able to provide the
answers

• What is an immune system for?

• How many necrosing cells does it take to cause
immune activation?

• Engage in the lab with immunologists to iteratively
reﬁne conceptual models

• Understand the mechanisms by observing the cell
behaviour ﬁrst hand

• Understand the key players in the processing of
Danger Signals


Introducing Dendritic Cells

• Process a set of signals

• Collect antigen proteins

• Combine suspect antigens
with signal evidence

• Make decisions for the
immune systems

• Relay this information to T-
cells who initiate immune
response


Development of the Dendritic Cell Algorithm



Semi-mature

Immature
Safe
Signals -present
antigen
-produce
costimulation
-provide
tolerance
cytokines
-in
lymph
node

Danger
Signals Mature

-collect
antigen PAMPS
-receive
signals
-in
tissue

-present
antigen
-produce
costimulation
-provide
reactive
cytokines
-in
lymph
node



DC created from monocyte

Immature DC

Resident: tissue
Antigen: collect
Express: IL-2
T-cell: no action

exposed to exposed to PAMP,
safe signals and danger signals and
inflammation inflammation

semi-mature DC mature DC

Resident: lymph node Resident: lymph node
Antigen: present Antigen: present
Express: CSM, IL-10 Express: CSM, IL-2
T-cell: supress T-cell: activate


Semi-mature

Immature
Safe
Signals -present
antigen
-produce
costimulation
-provide
tolerance
cytokines
-in
lymph
node

Danger
Signals Mature

-collect
antigen PAMPS
-receive
signals
-in
tissue

-present
antigen
-produce
costimulation
-provide
reactive
cytokines
-in
lymph
node


DC created from monocyte

Immature DC

Resident: tissue
Antigen: collect
Express: IL-2
T-cell: no action

exposed to exposed to PAMP,
safe signals and danger signals and
inflammation inflammation

semi-mature DC mature DC

Resident: lymph node Resident: lymph node
Antigen: present Antigen: present
Express: CSM, IL-10 Express: CSM, IL-2
T-cell: supress T-cell: activate


Input Data

‘Tissue’
- Storage area for data

S1 Ag1

S2 Ag2
Signal Matrix Antigen
behavioural signals S3 Ag3 collected data
(network ow) ... ... (process IDs)
Sn Agn

Data Sampling
Phase

Immature Dendritic Cell Population

Maturation
Phase
more safe more danger
signals signals

‘Semi-Mature’ ‘Mature’

Analysis


DC-inspired Signal Processing


Port Scan Detection Example

• Combine system call information with host based and network
statistics

• Detection of insider attacks through the assignment of anomaly
scores to process IDs

• Anomaly score generated through correlation between antigen
(system calls) and signals (system statistics)

• Produced less than 5% false positives for a variety of scans
performed in real time on a real network


Applications of the DCA @ Nottingham

• Intrusion detection systems based on behavioural analysis

• Insider attack detection for SYN port scans

• Out-performed Self Organising Maps

• Botnet and zombie machine detection

• Machine Learning KDD Cup ‘99 dataset

• Biofeedback device signal processing

• Autonomous mobile robotics


The Trouble With The DCA

• Notoriously difficult to understand (allegedly...) how to perform the mappings
between the problem domain and the signals and antigen

• Incorrectly implemented and applied to static machine learning problems

• Awful hybridizations including direct coupling with bad implementations of
negative selection

• Complex to analyse due to asycnchronous correlation between antigen and
signals, as this influences the classification accuracy of the technique

• Dr. Feng Gu has produced theoretical proofs and verifications, and
automatic problem domain interfacing techniques

• Its always the signal processing equation using a single cell which is
analysed, not really represenative of the algorithm - that part is simply a
linear classifier!


Despite This...

• It is an interesting technique, with potential in hard real-time
applications

• Through 3 years of analysis we ﬁnally understand its subtleties

• Has inspired others to take a similar approach and develop
population based efﬁcient algorithms in AIS

• RDA algorithm - Owen & Timmis (York 2010)

• Perhaps develop DCA 2.0?

• still does not look like I had originally envisioned it!!


So what is an AIS???

CLONALG BCA Opt-IA
DeCastro 2000 Kelsey et al 2003 Cutello 2007
JISYS AINNE/RAIN AI-NET AIRS
Parallel AIRS
Timmis et al. 1998 Timmis et al. 2000 De Castro 2000 Watkins 2004
Watkins 2004

Idiotypic Networks Idiotypic Networks Idiotypic Networks
For Robotics For AIS For Robotic Control
Watanabe et al. 1998 Hart & Ross 2002 Whitbrook et al. 2008

‘Clonal Selection Immune Networks
And After’ Bersini & Varela Modeling Viral Dynamics Stochastic Immune Responses
Burnet 1978 1991 Beauchemin et al. 2006 Salazar-Bañuelos 2008

‘Immune System, `Tending Adam’s Tunable Activation
Adaptation And Garden’ Conceptual Framework Thresholds
Machine Learning’ Cohen 2004 Stepney et al. 2004 Owen et al 2008
Farmer et al. 1986

Selection Model Receptor Degeneracy
Perelson et al. 1993 Andrews & Timmis 2007

Key Libtissue
Self-Nonself Danger Theory Twycross & Aickelin 2006
Clonal Selection Discrimination Aickelin & Cayzer The Danger Project Innate Immunity
Immune Networks Forrest et al. LISYS Secker et al. Aickelin et al. Twycross 2007
1994 Hoffmeyr 2000 2003 2004-8
Computational Models
2nd Generation Systems The Dendritic Cell The Deterministic DCA
Danger Based Algorithm (DCA) Greensmith & Aickelin 2008
Greenmsith 2007
Alternative Approaches Real Valued Negative Selection V-Detectors
Negative Selection Dasgupta et al 2000 Dasgupta & Ji 2004
‘Immunity by Design’
Forrest & Hofmeyr
1999

Empirical Studies of RIOT Negative Selection for Theoretical Studies of
Computer Immunology IBM AIS Negative Selection Batlthrop 2004 Fault Tolerance Negative Selection
Burgess 1999 Kephart 1999 Kim & Bentley 2001-2 Ayara et al 2004 Stibor et al 2005-7


Where to next?


While I was ﬁguring it out...


A Discipline of Two Halves
Computational Immuno-Engineering
Immunology


Generating better models

• It is hypothesised that if we can generate better understanding
of the immune system itself, then we can produce improved AIS

• Blossomed into a parallel track of AIS

• Unlike Immunoengineering, this is the application of computer
science and applied maths for the purpose of understanding
biological systems

• Some of the most novel, innovative and exciting work in AIS is
currently in computational immunology


Immune System Simulation

• Immune System has numerous desirable properties which we wish
to understand and explore in detail

• Capture the complex properties of the human immune system to
understand how the immune system can be manipulated in clinical
practice

• Shift away from a reductionist approach

• Enhanced understanding of systems biology

• Mathematical and computational modelling


Techniques in Computational Immunology

• Agent based modelling

• popular because of the autonomous and decentralised nature of the
human immune system

• expansion into multi-scale modelling

• Ordinary Differential Equations / System Dynamics

• already employed in theoretical immunology so transfers nicely

• Stochastic Pi Calculus and Process Algebra allow for the creation of
formal models for distributed interaction


Some Examples

• Dr. Grazziella Figuredo investigation of immune system ageing
through agent based and system dynamics models

• Elucidation of the role of MHC-II Antigen loading and presentation
mechanisms (Microsoft Research)

• Mechanisms of Granuloma formation and lymphoid tissue growth (U.
York)

• Trafﬁcking of Dendritic Cells to the Lymph node (Prof. Rob DeBoer)

• Modelling of the role of regulatory T-cells (U. York)


A Time Delay

• None of the research highlighted is sufﬁciently mature to feed
into AIS

• Wet lab experiments, simulation and modelling all take a lot of
time, including the notoriously difﬁcult task of model validation

• Would it be right to abstract from potentially wrong models?

• How can we develop sound principles for the abstraction
process?

• Currently unknown how computational immunology will link up
with AIS....


My First Grant
• Spent a number of years exploring and contemplating a
sustainable and exciting research direction

• Aim to follow computational immunology down a systems
thinking route of development

• Be able in 5 years time to confidently answer the question:
“What is an Artificial Immune System”

• Develop Artificial Immune Systems, no, really!

• Need an Application Platform to develop the desired AISs


If we create systems, not algorithms can we
captialize on the most desirable properties of
the human immune system for our artiﬁcial
problem solvers?


Learning From Ensembles

• DCA’s most beneficial properties from ensemble method components

• Ensemble classifiers are performing exceptionally well in a number of
problems involving noise and dynamic data analysis

• Two types of ensemble: homogenous and heterogenous

• Have multiple different classifiers examining the same data from different
perspectives

• like combining the accounts of different witnesses during a crime

• Ensemble methods and voting mechanisms could teach AIS a trick or two

• Develop multi-cell type AIS based on the principles of ensemble methods


Types of Ensemble Method


ARIES: ARtiﬁcial Immune EnsembleS

• Step 1: Create Homogeneous Ensembles

• An ensemble of multiple instances of the same algorithm

• Repeat using different ensemble methods and for each AIS

• Step 2: Create Heterogeneous Ensembles

• Create ensembles of different AIS in varying proportions

• Identify the most synergistic combinations of algorithm


Finding An Application Platform

• Previously application is inherent e.g. lets build an antivirus

• Plan to investigate application domain space iteratively as the ARIES systems are
developed

• Engagement with an Industrial User Base and social science methods to elicit laten
user needs

• ITuna - Biosensing devices

• General Dynamics - security and defence

• Esendex - IT and Telecoms

• Active Ingredient Data driven performance art


Potential Impact

• Expand from simple ensembles to looking at explicit pairing of multiple
interacting immune components within AIS

• A Paradigm Shift in the way in which AIS are developed

• Generation of robust and resilient systems

• Solving some of the most pertinent problems including cyber defence

• Engaging with Local Business through Esendex and Active Ingredient

• A systemic approach in other areas of computational intelligence??


Summary

• Artiﬁcial Immune Systems are not a single algorithm

• systems inspired by the human immune system

• Interdisciplinary processes inﬂuence their development

• the Dendritic Cell Algorithms

• Use Immune-inspired ensembles as a basis for developing a
systemic approach to the creation of future AIS methods

• In parallel, develop an affective computing framework as a
testing and exploration application


Thanks for the invite!

• Prof. Uwe Aickelin supervised and funded the majority of this research

• Dr. Jamie Twycross, Dr. Jungwon Kim (UCL/LG), Dr. Peter Bentley, Dr.
Rachel Harry, Dr. Charlotte Williams, Prof Julie McLeod and Dr. Steve
Cayzer

• Dr. Feng Gu, Dr. Jan Feyereisl, Dr. Robert Oates, Dr. Amanda Whitbrook,
Dr. Yousef Al-Hammadi and Gianni Tedesco

• EPSRC grant numbers: EP/F066910/1 EP/D071976/1

• U. Notts Anne McLaren Fellowship Scheme

julie.greensmith@nottingham.ac.uk
@fragglberri

Inspiration to Application: A Tutorial on Artificial Immune Systems

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