1. Artificial Immune Systems: An Emerging Technology Dr. Jonathan Timmis Computing Laboratory University of Kent at Canterbury England. UK. J.Timmis@ukc.ac.uk http:/www.cs.ukc.ac.uk/people/staff/jt6 Congress on Evolutionary Computation 2001. Seoul, Korea.

2. CEC 2001Artificial Immune Systems Tutorial Overview What are Artificial Immune Systems? Background immunology Why use the immune system as a metaphor Immune Metaphors employed Review of AIS work Applications More blue sky research

3. CEC 2001Artificial Immune Systems Immune metaphors Immune System Idea!Idea ‘ Other areas Artificial Immune Systems

4. CEC 2001Artificial Immune Systems Relatively new branch of computer science Some history Using natural immune system as a metaphor for solving computational problems Not modelling the immune system Variety of applications so far … Fault diagnosis (Ishida) Computer security (Forrest, Kim) Novelty detection (Dasgupta) Robot behaviour (Lee) Machine learning (Hunt, Timmis, de Castro)

5. CEC 2001Artificial Immune Systems Why the Immune System? Recognition Anomaly detection Noise tolerance Robustness Feature extraction Diversity Reinforcement learning Memory Distributed Multi-layered Adaptive

6. CEC 2001Artificial Immune Systems Part I – Basic Immunology

7. CEC 2001Artificial Immune Systems 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

8. CEC 2001Artificial Immune Systems How does it work?

9. CEC 2001Artificial Immune Systems Where is it?

10. CEC 2001Artificial Immune Systems Multiple layers of the immune system

11. CEC 2001Artificial Immune Systems 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

12. CEC 2001Artificial Immune Systems Antibodies Antibody MoleculeAntibody Production

13. CEC 2001Artificial Immune Systems Clonal Selection

14. CEC 2001Artificial Immune Systems T-cells Regulation of other cells Active in the immune response Helper T-cells Killer T-cells

15. CEC 2001Artificial Immune Systems 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

16. CEC 2001Artificial Immune Systems 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

17. CEC 2001Artificial Immune Systems Learning (2)

18. CEC 2001Artificial Immune Systems Immune Network Theory Idiotypic network ( Jerne, 1974) B cells co-stimulate each other Treat each other a bit like antigens Creates an immunological memory

19. CEC 2001Artificial Immune Systems Immune Network Theory(2)

20. CEC 2001Artificial Immune Systems Shape Space Formalism Repertoire of the immune system is complete ( Perelson, 1989) Extensive regions of complementarity Some threshold of recognition  V   V   V  V       

21. CEC 2001Artificial Immune Systems 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

22. CEC 2001Artificial Immune Systems Summary so far …. Immune system has some remarkable properties Pattern recognition Learning Memory So, is it useful?

23. CEC 2001Artificial Immune Systems Some questions for you !

24. CEC 2001Artificial Immune Systems Part II – A Review of Artificial Immune Systems

25. CEC 2001Artificial Immune Systems Topics to Cover A few disclaimers … I can not cover everything as there is a large amount of work out there To do so, would be silly Proposed general frameworks Give an overview of significant application areas and work therein I am not an expert in all the problem domains I would earn more money if I was !

26. CEC 2001Artificial Immune Systems Shape Space Describe interactions between molecules Degree of binding between molecules Complement threshold Each paratope matches a certain region of space Complete repertoire

27. CEC 2001Artificial Immune Systems Representation and Affinities Representation affects affinity measure Binary Integer Affinity is related to distance Euclidian Hamming Affinity threshold

28. CEC 2001Artificial Immune Systems Basic Immune Models and Algorithms Bone Marrow Models Negative Selection Algorithms Clonal Selection Algorithm Somatic Hypermutation Immune Network Models

29. CEC 2001Artificial Immune Systems 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

30. CEC 2001Artificial Immune Systems 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

31. CEC 2001Artificial Immune Systems 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)

32. CEC 2001Artificial Immune Systems 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 ,

33. CEC 2001Artificial Immune Systems 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

34. CEC 2001Artificial Immune Systems 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

35. CEC 2001Artificial Immune Systems Part III - Applications

36. CEC 2001Artificial Immune Systems 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

37. CEC 2001Artificial Immune Systems Virus Detection Protect the computer from unwanted viruses Initial work by Kephart 1994 More of a computer immune system

38. CEC 2001Artificial Immune Systems 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

39. CEC 2001Artificial Immune Systems 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 SystemComputational System Pathogens (antigens)Computer viruses B-, T-cells and antibodiesDetectors ProteinsStrings Antibody/antigen bindingPattern matching

40. CEC 2001Artificial Immune Systems Security Somayaji et al. (1997) outlined mappings between IS and computer systems A security systems need Confidentiality Integrity Availability Accountability Correctness

41. CEC 2001Artificial Immune Systems IS to Security Systems Immune SystemNetwork Environment Static Data SelfUncorrupted data Non-selfAny change to self Active Processes on Single Host CellActive process in a computer Multicellular organismComputer running multiple processes Population of organismsSet of networked computers Skin and innate immunitySecurity mechanisms, like passwords, groups, file permissions, etc. Adaptive immunityLymphocyte process able to query other processes to seek for abnormal behaviors Autoimmune responseFalse alarm SelfNormal behavior Non-selfAbnormal behavior Network of Mutually Trusting Computers Organ in an animalEach computer in a network environment

42. CEC 2001Artificial Immune Systems 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 (1999). New paper here at CEC so I won’t cover it, go see it for yourself!

43. CEC 2001Artificial Immune Systems Forrests Model AIS for computer network security. (a) Architecture. (b) Life cycle of a detector. Datapathtriple (20.20.15.7, 31.14.22.87, ftp) Broadcast LAN ip: 31.14.22.87 port: 2000 Internal host External host ip: 20.20.15.7 port: 22 Host Activation threshold Cytokine level Permutation mask Detector set immature memory activated matches 0100111010101000110......101010010 Detector Randomlycreated Immature Mature & Naive Death Activated Memory No matchduring tolerization 010011100010.....001101 Exceed activation threshold Don’t exceed activation threshold No co stimulation Co stimulation Match during tolerization

44. CEC 2001Artificial Immune Systems Novelty Detection Image Segmentation : McCoy & Devarajan (1997) Detecting road contours in aerial images Used a negative selection algorithm

45. CEC 2001Artificial Immune Systems Hardware Fault Tolerance Immunotronics (Bradley & Tyrell, 2000) Use negative selection algorithm for fault tolerance in hardware Table 4.1. Immune SystemHardware Fault Tolerance Recognition of selfRecognition of valid state/state transition Recognition of non-selfRecognition of invalid state/state transition LearningLearning correct states and transitions Humoral immunityError detection and recovery Clonal deletionIsolation of self-recognizing tolerance conditions Inactivation of antigenReturn to normal operation Life of an organismOperation lifetime of a hardware

46. CEC 2001Artificial Immune Systems 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

47. CEC 2001Artificial Immune Systems 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

48. CEC 2001Artificial Immune Systems Results (Timmis, 2000)

49. CEC 2001Artificial Immune Systems 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

50. CEC 2001Artificial Immune Systems Results (de Castro & von Zuben, 2001) Test ProblemResult from aiNET

51. CEC 2001Artificial Immune Systems 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 System can generalise Good classification rates on sample data sets

52. CEC 2001Artificial Immune Systems 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

53. CEC 2001Artificial Immune Systems 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.

54. CEC 2001Artificial Immune Systems 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.

55. CEC 2001Artificial Immune Systems 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

56. CEC 2001Artificial Immune Systems The Future Rapidly growing field that I think is very exciting Much work is very diverse Need of a general framework Wide possible application domains Lots of work to do …. Keep me in a job for quite a while yet