Inspiration to Application: A Tutorial on Artificial Immune Systems

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A tutorial of the history and application of artificial immune systems, given as a research tutorial for the Intelligent Modelling and Analysis Research Group, School of Computer Science, University of Nottingham UK.

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Inspiration to Application: A Tutorial on Artificial Immune Systems

  1. 1. From Inspiration to Application: Artificial Immune Systems Dr. Julie Greensmith Intelligent Modelling & Analysis School of Computer Science julie.greensmith@nottingham.ac.ukTuesday, 6 December 11
  2. 2. 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 AISTuesday, 6 December 11
  3. 3. 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 BandTuesday, 6 December 11
  4. 4. 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 applicationsTuesday, 6 December 11
  5. 5. A Short History Of Artificial Immune SystemsTuesday, 6 December 11
  6. 6. Why Be Bio-inspired?Tuesday, 6 December 11
  7. 7. 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, optimizationTuesday, 6 December 11
  8. 8. 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?Tuesday, 6 December 11
  9. 9. 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 biologyTuesday, 6 December 11
  10. 10. Negative SelectionTuesday, 6 December 11
  11. 11. 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 223-25Tuesday, 6 December 11
  12. 12. 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 infeasibleTuesday, 6 December 11
  13. 13. Clonal Selection yes, its pretty much a GA without crossoverTuesday, 6 December 11
  14. 14. 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 predictionTuesday, 6 December 11
  15. 15. 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”Tuesday, 6 December 11
  16. 16. Idiotypic Network for Robotics • A. Whitbrook & U. Aickelin (2006-2008) in IMATuesday, 6 December 11
  17. 17. 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....Tuesday, 6 December 11
  18. 18. Tuesday, 6 December 11
  19. 19. 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 AISTuesday, 6 December 11
  20. 20. Our Second Approach: We Talked to Immunologists (2004-2010)Tuesday, 6 December 11
  21. 21. 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)Tuesday, 6 December 11
  22. 22. 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 studentTuesday, 6 December 11
  23. 23. 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 presentationTuesday, 6 December 11
  24. 24. Necrosis Versus ApoptosisTuesday, 6 December 11
  25. 25. 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?Tuesday, 6 December 11
  26. 26. 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 SignalsTuesday, 6 December 11
  27. 27. 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 responseTuesday, 6 December 11
  28. 28. Development of the Dendritic Cell AlgorithmTuesday, 6 December 11
  29. 29. 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 nodeTuesday, 6 December 11
  30. 30. Development of the Dendritic Cell Algorithm 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: activateTuesday, 6 December 11
  31. 31. Development of the Dendritic Cell AlgorithmTuesday, 6 December 11
  32. 32. 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 nodeTuesday, 6 December 11
  33. 33. 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: activateTuesday, 6 December 11
  34. 34. Tuesday, 6 December 11
  35. 35. 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’ AnalysisTuesday, 6 December 11
  36. 36. DC-inspired Signal ProcessingTuesday, 6 December 11
  37. 37. 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 networkTuesday, 6 December 11
  38. 38. 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 roboticsTuesday, 6 December 11
  39. 39. 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!Tuesday, 6 December 11
  40. 40. 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!!Tuesday, 6 December 11
  41. 41. 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 2005-7Tuesday, 6 December 11
  42. 42. Tuesday, 6 December 11
  43. 43. Where to next?Tuesday, 6 December 11
  44. 44. While I was figuring it out...Tuesday, 6 December 11
  45. 45. A Discipline of Two Halves Computational Immuno-Engineering ImmunologyTuesday, 6 December 11
  46. 46. 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 immunologyTuesday, 6 December 11
  47. 47. 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 modellingTuesday, 6 December 11
  48. 48. 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 interactionTuesday, 6 December 11
  49. 49. 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)Tuesday, 6 December 11
  50. 50. 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....Tuesday, 6 December 11
  51. 51. 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 AISsTuesday, 6 December 11
  52. 52. If we create systems, not algorithms can we captialize on the most desirable properties of the human immune system for our artificial problem solvers?Tuesday, 6 December 11
  53. 53. 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 methodsTuesday, 6 December 11
  54. 54. Types of Ensemble MethodTuesday, 6 December 11
  55. 55. 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 algorithmTuesday, 6 December 11
  56. 56. 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 artTuesday, 6 December 11
  57. 57. 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??Tuesday, 6 December 11
  58. 58. 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 applicationTuesday, 6 December 11
  59. 59. 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 @fragglberriTuesday, 6 December 11

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