From: Subject: Logical Genetics Date: Sat, 21 Aug 2004 18:34:00 +0300 MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_NextPart_000_0009_01C487AD.6D7AC1B0"; type="text/html" X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2800.1441 This is a multi-part message in MIME format. ------=_NextPart_000_0009_01C487AD.6D7AC1B0 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Content-Location: http://www.logicalgenetics.com/aisintro/aisintro.html Logical = Genetics
3D"Logical 3D"Logical 3D"Logical 3D"Logical 3D"Logical    3Dlg.gif=20=20

Artificial Immune Systems

Dan Taylor = (dan@logicalgenetics.com)

Abstract:=20 In recent months the study of Artificial Immune Systems = (AIS) has=20 become more and more popular amongst the academic = community. The=20 first conference on AIS (ICARIS) was held in Canterbury in 2002 = and there=20 have been sessions and publications on AIS at most recent=20 conferences on artificial intelligence. This article gives = an=20 insight into the power of AIS as a problem solving = technique,=20 focussing on the development of an AIS component set for = Delphi.=20 Two real-world examples are presented: fault prediction in = refrigerators and change detection in Linux configuration = files.=20

Contents

  • Introduction=20
  • The=20 Human Immune System
  • Properties=20 of the Immune System
  • Architecture=20 of an Artificial Immune System
  • The=20 AIS Component Set
    • = Class=20 Diagrams
    • P= attern=20
    • Pattern=20 Set
    • Matching=20 Rule
    • Self=20 Set Factory
    • Detector=20 Set Factory
    • Change=20 Monitor
    • = AIS=20 Test Cases
  • Refr= igeration=20 Fault Prediction Demo
    • = The=20 Problem
    • T= he=20 Application
  • Linu= x=20 Hacker Detection Demo
  • Conclusions=20
  • Acknowledgements=20
  • Link= s=20 and Resources

Introduction

The aims of this article are twofold. Firstly, the = article gives=20 an introduction to Artificial Immune Systems (AIS), their = operation=20 and biological inspiration. Secondly it is hoped that the = component=20 framework developed here will provide a sound foundation for = Delphi=20 developers wishing to exploit AIS technology.

We start by giving a brief overview of the human immune = system,=20 its distributed architecture and the in built mechanisms for = detecting intruders by self/ non self = discrimination. The=20 next section lists the computational aspects of the immune = system=20 which form the basis of data structures and algorithms we = might use=20 to represent it. The architecture of an artificial immune = system is=20 outlined in the next section, followed by details of the = components=20 that were developed for this article. These components are = used in=20 two demo applications which exploit the capabilities of AIS = in very=20 different ways. Finally we conclude with a discussion of how = the AIS=20 approach differs from other AI systems.

To= p=20 of Page

The Human Immune System

The purpose of the human immune system is twofold: It = must be=20 able to detect and categorise foreign bodies = (antigens) and=20 must also be able to destroy or neutralise them to protect = us from=20 their effects. We will concentrate on the first of these, = the=20 ability to detect and categorise antigens. The term antigen = refers=20 to anything which is not part of the body itself, including=20 bacteria, viruses and suchlike. The job of the immune system = is to=20 differentiate between antigens and the body itself, = initiating the=20 appropriate immune response for foreign bodies, whilst = leaving=20 everything else untouched. This is known as "self/ non-self=20 discrimination".

The main component of the immune system is the=20 lymphocyte. Countless millions of these circulate = through=20 the bloodstream and within the tissues of the body. = Lymphocytes are=20 able to recognise non-self (i.e. antigens) by binding to = them with=20 chemical bonds as illustrated in figure 1. The more = complimentary=20 the antigen and lymphocyte the higher the likelihood of a = bond=20 forming. The probability of a bond forming is known as = affinity -=20 high affinity is a high likelihood of a bond forming.

3D"Figure = Figure=20 1: An antigen binding to a lymphocyte with an exact = match

The binding shown in figure 1 is an exact match. However, = to=20 ensure that the immune system can recognise as many antigens = as=20 possible an exact match is not required. A match can occur = if a=20 given number of contiguous features complement each other. = The=20 number of features required to bind before a match can be = made is=20 known as the affinity threshold. Figure 2 shows a lymphocyte = and=20 antigen which match, though not perfectly - there are four=20 contiguous matching bonds (numbers 2 to 5). Figure 3 shows a = lymphocyte which does not match the antigen as there are no = sets of=20 contiguous features long enough to cause a match (assuming = our=20 affinity threshold is greater than two).

3D"Figure=20 Figure=20 2: An antigen binding to a lymphocyte with a = partial=20 match

3D"Figure = Figure=20 3: An antigen and lymphocyte which do not = match=20

There are two types of lymphocytes: B cells and T cells. = We are=20 only interested in T cells here. All lymphocytes have three = stages=20 in their lifecycle.

  1. Naive lymphocytes have not yet been "trained" to = recognise a=20 specific antigen=20
  2. Effector lymphocytes are active within the immune = system=20
  3. Memory lymphocytes exist only to store patterns = associated=20 with antigens that have invaded the body in the past =

Lymphocytes are "born" in the lymphoid organs through the = division of existing lymphocyte cells. An unusually large = level of=20 mutation takes place as these lymphocytes divide (known as=20 Somatic Hypermutation). This means that "child" = lymphocytes=20 are different, though similar to their "parents". This = mutation=20 allows the immune system to learn to categorise new threats. = The=20 process is known as clonal expansion.

During clonal expansion it is possible that new = lymphocytes will=20 be created which incorrectly categorise proteins in the body = (self)=20 as antigens (non-self). These must not be allowed to = circulate=20 around the body as they will cause the immune system to = attack the=20 body's tissues. Therefore, lymphocytes are subjected to a = negative=20 selection process in which they are tested to ensure they do = not=20 categorise self proteins. Lymphocytes which fail the test = are=20 destroyed, those which pass are allowed to circulate around = the=20 body.

When a new antigen invades the body only a few = lymphocytes are=20 able to recognise it. The body must quickly produce more = lymphocytes=20 with high affinity to the antigen, before it has had time to = damage=20 the body too much. For this reason lymphocytes with a high = affinity=20 for antigens are encouraged to reproduce more, that is to = say that=20 recognition stimulates cells to reproduce and mutate. Cells = which=20 mutate to find a better match with the target antigen are = encouraged=20 to divide more. This process creates a large number of = lymphocytes=20 which are specific to the antigen.

The process of going from a reasonably low affinity to an = antigen=20 in a few lymphocytes to produce a large population of high = affinity=20 lymphocytes is called Affinity Maturation. This = process=20 uses reproduction and mutation to create new cells and = favours=20 higher affinity (fitter) cells for reproduction. = Essentially,=20 affinity maturation is a Darwinian evolutionary process. =

Once an antigen has been destroyed, the immune system = will=20 remember it, in case it enters the body in future. This = memory comes=20 about through memory lymphocytes, which are created in the = same was=20 as effector lymphocytes but have a longer life span. Memory = cells=20 serve as a reminder of antigen patterns which have posed a = threat in=20 the past. Because memory cells have a high affinity for a = given=20 antigen they are more likely to reproduce. Thus a subset of = the=20 lymphocyte population will continue to have a high affinity = for the=20 remembered antigen.

So, the immune system is diverse in that a large number = of=20 different antigens can be detected, protecting the body from = just=20 about anything which might invade it. The negative selection = process=20 ensures that lymphocytes do not attack the body itself by = checking=20 all new lymphocytes against a set of "self" proteins. It can = quickly=20 respond to a new antigen because lymphocytes with high = affinities=20 are encouraged to reproduce more. The immune system is also = able to=20 remember antigens it has dealt with in the past, by keeping = a=20 population of memory lymphocytes in circulation.

Properties of the Immune System

Some properties of the immune system, computational and=20 otherwise, are enumerated below.

  • Recognition: Self/ non-self = categorisation=20 takes place based on intercellular binding=20
  • Diversity: A diverse set of = lymphocytes is=20 created, partly by genetic process, to ensure that cells = exist=20 which can bind to any antigen - known or unknown=20
  • Learning Clonal expansion, during the = first=20 encounter with an antigen allows the immune system to = learn, by=20 example, to recognise specific antigens=20
  • Memory: Memory cells store the = patterns=20 associated with antigens encountered in the past=20
  • Distributed Detection: The immune = system is=20 inherently distributed. Lymphocytes and continually = recirculated=20 around the body where they encounter antigenic challenges = and=20 stimulate the appropriate immune response=20
  • Self Regulation: Regulation takes = place=20 either locally or on a system wide basis, depending on the = situation=20
  • Threshold Mechanism: Immune system = response=20 takes place above a threshold, dictated by the strength of = the=20 chemical binding between lymphocyte and antigen=20
  • Dynamic Protection: Affinity = maturation=20 (clonal expansion and somatic hyper-mutation) allows the=20 generation of high affinity lymphocytes. This dynamically = balances=20 exploration versus exploitation in immunity. Dynamic = protection=20 increases the coverage of the immune system over time=20
  • Probabilistic Detection: The cross = reaction=20 in immune response is a stochastic process. Detection is=20 approximate, so one lymphocyte may bind to several = antigens=20

Architecture of an Artificial Immune System

The building blocks we need to create an artificial = immune system=20 are listed here. This section details the basic structure of = the=20 system, to clarify the link between the biological = inspiration and=20 the implemented system. The next section gives code listings = and=20 other implementation specific details for the Delphi = component set.=20

For any system, be it a computer system, manufacturing = process or=20 real time control application, we can define a pattern of = normal=20 behaviour. For example, a bridge will vibrate only within a = given=20 frequency and amplitude range and we can safely assume that = if the=20 vibration of the bridge leaves this normal range we are = heading=20 towards a potentially dangerous situation. Similarly, the = behaviour=20 of users and processes within a computer system will = generally fall=20 within an acceptable range of values for CPU, memory, = network and=20 resource usage. We are probably safe in assuming that if a = process=20 leaves this range there is something bad happening. Maybe = that=20 process has crashed or a user is attempting to do something=20 untoward.

Artificial Immune Systems are an ideal tool for detecting = these=20 sorts of incidents. We know from the previous section that = the human=20 immune system is able to differentiate between "self" and = "non-self"=20 and we can use this ability in a computer system to detect = when=20 things go wrong. We define "self" to be the normal operation = of the=20 system. For the bridge example above, "self" is the normal = range of=20 vibrations, loads and suchlike. For the computer security = example,=20 "self" is the normal range of process behaviour. "Non-self" = in both=20 examples is any measurement we make that is outside these = ranges.=20

3D"Figure = Figure=20 4: Normal behaviour of a system

Figure 4 shows an example measurement for a system which = is=20 functioning normally. It could be a vibration measurement of = our=20 bridge as somebody walks across it. (Actually I just used = Matlab to=20 add a couple of sine waves together to give a sensible = example).=20 Figure 5 shows another example measurement for the same = system, but=20 this time there is a noise signal there which represents a = fault.=20 Perhaps the wind is making our bridge unstable? We need out = bridge's=20 immune system to be able to sense this change in behaviour = and raise=20 an alarm so we can investigate the problem.

3D"Figure = Figure=20 5: Abnormal behaviour of a system =

The first thing we need to define is a Self Set. = The=20 self set is a set of patterns which represent normal = operation of=20 the system. These patterns will be used by the negative = selection=20 process to ensure that our artificial lymphocytes (T cells) = do not=20 detect "self" (i.e. to ensure that they don't raise an alarm = under=20 normal operation). Our self set will be simply a set of = symbol=20 sequences. Our lymphocytes will also be a set of symbol = sequences.=20 Matching will be measured simply by comparing symbols. The = symbols=20 we'll use here are positive integers. Different problems = will need a=20 different number of symbols, some, such as comparing binary = strings,=20 will need only two (1 and 0), while others will require = more. Figure=20 6 shows how we would convert a continuous sensor reading = into a=20 series of symbols.

3D"Figure=20 Figure=20 6: Normal behaviour of a system - restricted to 10 = symbols=20 (0 - 9)

Next we create a library of "self" patterns by chopping = the above=20 into a set of shorter sequences. The self set for the above = might=20 look something like listing 1. 

  =
Pattern 1:  5     6     6     7     8     8     9
  Pattern 2:  9     9     9     9     9     9     9
  Pattern 3:  8     8     7     7     6     6     5
  Pattern 4:  5     5     4     4     4     4     4
Listing=20 1: Example self set with 4 patterns and 10 symbols = (0 - 9)=20

Once we have our self set we are ready to start creating = the=20 lymphocytes which will monitor the system. New lymphocytes = must be=20 presented to self patterns to see if they "match" or not. = Listing 2=20 is an example of how the negative selection algorithm will = compare=20 new lymphocytes to self patterns. Remember that any = lymphocyte that=20 matches any self pattern will be destroyed. The length of = the=20 lymphocytes and the self patterns must be the same and it, = in this=20 example, 7. 

Exact match - delete =
lymphocyte

  Self pattern:  1 6 7 3 6 2 2
    Lymphocyte:  1 6 7 3 6 2 2

Partial match (with threshold of 4) - delete lymphocyte

  Self pattern:  1 6 7 3 6 2 2
    Lymphocyte:  4 6 7 3 6 3 9

No match (with threshold of 4) - keep lymphocyte

  Self pattern:  1 6 7 3 6 2 2
    Lymphocyte:  1 6 1 3 6 1 2
Listing=20 2: Matching self set patterns to lymphocytes=20

We will use the algorithm in listing 3 to create a set of = lymphocytes known as the Detector Set. 

  repeat
    create a new lymphocyte pattern at random
    add lymphocyte to detector set
    for each pattern in the self set
      if lymphocyte matches self set pattern
        delete lymphocyte from detector set
        break
      end
    end
  until size limit reached
Listing=20 3: Negative selection algorithm for creating the = detector=20 set

Once we have generated our detector set we are ready to = start=20 looking for abnormal system behaviour. We do this by = discreetising=20 new data from our system using the rules above, ensuring = that we use=20 the same symbol set, pattern size and suchlike. We can then = watch=20 for errors/ faults by continually applying every artificial=20 lymphocyte in our detector set to the new data. Because our = detector=20 set is self tolerant (i.e. will never match normal data) we = can be=20 sure that if any detector matches our new data then that = data is=20 indicative of abnormal behaviour. To put it another way: If = a=20 detector matches we have a fault.

To summarise, the following is a list of the components = of an=20 artificial immune system:

  • Symbols: We must be able to decompose = measurements of our systems into a small set of symbols = (the=20 smaller the better). For the sake of this article, symbols = are=20 positive integers and could be simply [0, 1] for binary, = [0 .. 35]=20 for numbers and letters (a..z, 0..9) or some other = appropriate=20 range.=20
  • Pattern: A pattern is a simple = structure for=20 storing a list of symbols. The self set and detector set = are=20 simply lists of patterns.=20
  • Self Set: A set of patterns, = constructed from=20 past data, which represents normal operation of our system =
  • Detector Set: A set of lymphocyte = patterns,=20 generated using the negative selection algorithm (above)=20

The AIS Component Set

Figures 8 shows the class diagram for the artificial = immune=20 system component set. Figure 7 shows the corresponding = interfaces.=20 You will notice that classes to represent the AIS building = blocks=20 detailed in the previous section have been implemented. A = couple of=20 other components have been created to perform other tasks. = The=20 following list is a (very) brief introduction to the AIS = component=20 set. The bulk of this section goes on to give detailed = descriptions=20 of each component, including code listings where = appropriate. The=20 final part of this section details the unit tests which were = created=20 for the AIS components.

  • Symbol: Symbols are represented as = integers.=20 Therefore there is no "symbol" class.=20
  • Pattern: The TAISPattern class stores = a list=20 of symbols using a dynamic array. Self patterns and = detectors/=20 lymphocytes are implemented as TAISPatterns.=20
  • PatternSet: The TAISPattern set = stores a list=20 of TAISPattern objects. TAISPatternSet is used to = represent the=20 self set and the detector set.=20
  • Self Set Factories Classes which = implement=20 IAISSelfSetFactory are used to load the appropriate self = sets from=20 files.=20
  • Detector Set Factory = TAISDetectorSetFactory=20 uses the negative selection algorithm to create a randomly = initialised set of detectors (artificial lymphocytes).=20
  • Matching Rule The TAISMatchingRule = component=20 is used to check whether two patterns match, based on a = matching=20 threshold. Both the TAISDetectorSetFactory and the=20 TAISChangeMonitor make use of the TAISMatchingRule.=20
  • Change Monitor The TAISChangeMonitor = applies=20 a detector set to a self set and reports the number and = position=20 of matches which occur.

Class Diagrams

Figures 7 and 8 show class diagrams for the AIS component = set.=20 The figures were exported from Model Maker.

3D"Figure=20 Figure=20 7: AIS Interfaces

3D"Figure=20 Figure=20 8: AIS Classes

Pattern

The TAISPattern component stores a list of integers. A = dynamic=20 array (FItems) is exposed through the array property = "Items". There=20 is no functionality for sorting, searching or suchlike, = simply=20 because such functionality is not required. 

  IAISPattern =3D interface
    function GetItem(I: Integer): Integer;
    procedure SetItem(I: Integer; const Value: Integer);
    function GetSize: Integer;
    procedure SetSize(const Value: Integer);
    property Size : Integer read GetSize write SetSize;
    property Items [I : Integer] : Integer read GetItem write SetItem;
  end;

  TAISPattern =3D class(TComponent, IAISPattern)
  private
    FItems : array of Integer;
    function GetItem(I: Integer): Integer;
    procedure SetItem(I: Integer; const Value: Integer);
    function GetSize: Integer;
    procedure SetSize(const Value: Integer);
  public
    constructor Create(AOwner : TComponent); override;
    destructor Destroy; override;
    property Size : Integer read GetSize write SetSize;
    property Items [I : Integer] : Integer read GetItem write SetItem;
  end;
Listing=20 4: Code for IAISPattern interface and TAISPattern = component=20

Pattern Set

The TAISPatternSet component stores a list of TAISPattern = components. The TAISPattern set is used to represent the = self set=20 and detector set in our artificial immune system. The = "Components"=20 and "ComponentCount" fields inherited from TComponent are = used for=20 storage. This has the benefit of providing garbage = collection at=20 no extra cost and also removes the need for an = additional TList=20 field for storage. ComponentCount and Components are exposed = using=20 the Count and Patterns properties respectively. 

unit UnitAISPatternSet;

interface

uses
  UnitAISPattern, Classes;

type

  IAISPatternSet =3D interface
    function GetPattern(I: Integer): IAISPattern;
    function GetCount: Integer;
    function AddPattern : IAISPattern;
    property Patterns [I : Integer] : IAISPattern read GetPattern;
    property Count : Integer read GetCount;
  end;

  TAISPatternSet =3D class(TComponent, IAISPatternSet)
  private
    function GetPattern(I: Integer): IAISPattern;
    function GetCount: Integer;
  public
    function AddPattern : IAISPattern;
    property Patterns [I : Integer] : IAISPattern read GetPattern;
    property Count : Integer read GetCount;
  end;


implementation

{ TAISPatternSet }

function TAISPatternSet.AddPattern: IAISPattern;
var
  New : TAISPattern;
begin
  New :=3D TAISPattern.Create(Self);

  Result :=3D New;
end;

end.
Listing=20 5: Code for IAISPatternSet interface and = TAISPatternSet=20 component

Matching Rule

The IAISMatchingRule interface is a template for classes = which=20 implement a matching rule of some sort. The TAISMatchingRule = component implements the interface to provide other = components=20 (namely the TAISDetectorSetFactory and TAISChangeMonitor) = with the=20 ability to compare patterns.

A partial matching rule is implemented, based on a = matching=20 threshold. Two patterns match if there are identical strings = with=20 length greater than or equal to the matching threshold in = each. The=20 function "Matches" takes two patterns and returns true or = false as=20 appropriate. If the function returns true a match has been = found.=20 

unit UnitAISMatchingRule;

interface

uses
  Classes, UnitAISPattern, Math;

type

  IAISMatchingRule =3D interface
    function GetMatchingThreshold: Integer;
    procedure SetMatchingThreshold(const Value: Integer);
    function Matches(P1, P2 : IAISPattern) : Boolean;
    property MatchingThreshold : Integer read GetMatchingThreshold
                                         write SetMatchingThreshold;
  end;

  TAISMatchingRule =3D class(TComponent, IAISMatchingRule)
  private
    FMatchingThreshold: Integer;
    function GetMatchingThreshold: Integer;
    procedure SetMatchingThreshold(const Value: Integer);
  public
    function Matches(P1, P2 : IAISPattern) : Boolean;
  published
    property MatchingThreshold : Integer read GetMatchingThreshold
                                         write SetMatchingThreshold;
  end;

procedure Register;

implementation

procedure Register;
begin
  RegisterComponents('AIS Components', [TAISMatchingRule]);
end;

{ TAISMatchingRule }

function TAISMatchingRule.Matches(P1, P2: IAISPattern): Boolean;
var
  i, Top, BitsMatching : Integer;
  Match : Boolean;
begin
  Top :=3D min(P1.Size, P2.Size);
  Match :=3D False;
  BitsMatching :=3D 0;

  for i :=3D 0 to Top - 1 do
  begin
    if P1.Items[i] =3D P2.Items[i] then
      inc(BitsMatching)
    else
      BitsMatching :=3D 0;

    if BitsMatching =3D FMatchingThreshold then
    begin
      Match :=3D True;
      Break;
    end;
  end;

  Result :=3D Match;
end;

end.
Listing=20 6: Code for IAISMatchingRule interface and = TAISMatchingRule=20 component

Self Set Factory

The ISelfSetFactory interface is a template for classes = which in=20 some way construct a self set. The most important member of = the=20 interface is the "CreateSelfSet" function, which is = responsible for=20 creating the self set. The only other properties are = BinCount and=20 PartitionSize. PartitionSize controls the size of the = patterns in=20 the pattern set to be constructed. BinCount is the number of = integer=20 symbols which should be used. The name "BinCount" was used = to=20 correspond to the means by which real values are = discreetised:=20 "values between 0 and 1 go in bin 1, values above 10 go = in bin 9=20 etc etc". A more appropriate property name might have = been=20 "SymbolCount".

The component TAISSelfSetFactory and its descendant=20 TAISSelfSetFromFileFactory are implementations of the=20 IAISSelfSetFactory interface. They are used to load data for = the=20 fridge fault detection example from ASCII data files = exported from=20 Matlab. 

  IAISSelfSetFactory =3D =
interface
    function GetPartitionSize: Integer;
    procedure SetPartitionSize(const Value: Integer);
    function GetBinCount: Integer;
    procedure SetBinCount(const Value: Integer);
    function CreateSelfSet(AOwner : TComponent) : IAISPatternSet;
    property PartitionSize : Integer read GetPartitionSize
                                     write SetPartitionSize;
    property BinCount : Integer read GetBinCount write SetBinCount;
  end;

  TAISSelfSetFactory =3D class(TComponent, IAISSelfSetFactory)
  private
    function GetPartitionSize: Integer;
    procedure SetPartitionSize(const Value: Integer);
    function GetBinCount: Integer;
    procedure SetBinCount(const Value: Integer);
  protected
    FBinCount : Integer;
    FPartitionSize : Integer;
  public
    function CreateSelfSet(AOwner : TComponent) : IAISPatternSet;
                                                 virtual; abstract;
    property PartitionSize : Integer read GetPartitionSize
                                     write SetPartitionSize;
    property BinCount : Integer read GetBinCount write SetBinCount;
  end;
Listing=20 7: Code for IAISSelfSetFactory interface and=20 TAISSelfSetFactory component

Detector Set Factory

The TAISDetectorSetFactory component is used to create = the=20 detector set for our artificial immune system. The=20 GenerateDetectorSet function returns a set of detector = patterns=20 which are generated at random. The negative selection = algorithm=20 presents each new candidate detector to every pattern in the = self=20 set. If a candidate detector matches any self set pattern = then it is=20 discarded and a new one is created. For this reason, the=20 relationship between the number of patterns to be created=20 (determined by the DetectorCount property) to the time taken = to=20 create the detector set is exponential. The matching = threshold also=20 has a great effect on the time taken to create the detector = set. As=20 the matching threshold decreases it is harder to generate a=20 detector, at random, which does not match any pattern in the = self=20 set.

Note that, as mentioned in the comments in listing 8, the = random=20 generation algorithm could be replaced by some heuristic to = create=20 more effective detectors. Also, there is currently no = functionality=20 for preventing the creation of identical detectors. Since = identical=20 detectors will match exactly the same set of patterns, the = existence=20 of identical detectors in the self set is a waste of space. = The more=20 duplicate detectors we have in our detector set the less = efficient=20 our artificial immune system becomes. However, eliminating = duplicate=20 detectors means comparing every candidate detector to the = self set=20 and the detector set. This causes a large increase = in the=20 time taken to generate the detector set. 

unit UnitAISDetectorSetFactory;

interface

uses Classes, UnitAISMatchingRule, UnitAISPatternSet, UnitAISPattern;

type

  IAISDetectorSetFactory =3D interface
    function GetBinCount: Integer;
    function GetMatchingRule: IAISMatchingRule;
    function GetPartitionSize: Integer;
    function GetSelfSet: IAISPatternSet;
    procedure SetBinCount(const Value: Integer);
    procedure SetMatchingRule(const Value: IAISMatchingRule);
    procedure SetPartitionSize(const Value: Integer);
    procedure SetSelfSet(const Value: IAISPatternSet);
    function GetDetectorCount: Integer;
    procedure SetDetectorCount(const Value: Integer);
    function NoMatches(Pattern : IAISPattern) : Boolean;
    procedure InitialiseRandomly(Pattern : IAISPattern);
    function GenerateDetectorSet(AOwner : TComponent) : IAISPatternSet;
    property MatchingRule : IAISMatchingRule read GetMatchingRule
                                             write SetMatchingRule;
    property SelfSet : IAISPatternSet read GetSelfSet
                                      write SetSelfSet;
    property PartitionSize : Integer read GetPartitionSize
                                     write SetPartitionSize;
    property BinCount : Integer read GetBinCount write SetBinCount;
    property DetectorCount : Integer read GetDetectorCount
                                     write SetDetectorCount;
  end;

  TAISDetectorSetFactory =3D class(TComponent, IAISDetectorSetFactory)
  private
    function GetBinCount: Integer;
    function GetMatchingRule: IAISMatchingRule;
    function GetPartitionSize: Integer;
    function GetSelfSet: IAISPatternSet;
    procedure SetBinCount(const Value: Integer);
    procedure SetMatchingRule(const Value: IAISMatchingRule);
    procedure SetPartitionSize(const Value: Integer);
    procedure SetSelfSet(const Value: IAISPatternSet);
    function GetDetectorCount: Integer;
    procedure SetDetectorCount(const Value: Integer);
    function NoMatches(Pattern : IAISPattern) : Boolean;
    procedure InitialiseRandomly(Pattern : IAISPattern);
  protected
    FMatchingRule : IAISMatchingRule;
    FSelfSet : IAISPatternSet;
    FBinCount : Integer;
    FPartitionSize : Integer;
    FDetectorCount : Integer;
  public
    function GenerateDetectorSet(AOwner : TComponent) : IAISPatternSet;
    property MatchingRule : IAISMatchingRule read GetMatchingRule
                                             write SetMatchingRule;
    property SelfSet : IAISPatternSet read GetSelfSet write SetSelfSet;
    property PartitionSize : Integer read GetPartitionSize
                                     write SetPartitionSize;
    property BinCount : Integer read GetBinCount write SetBinCount;
    property DetectorCount : Integer read GetDetectorCount
                                     write SetDetectorCount;
  end;

implementation

{ TAISDetectorSetFactory }

function TAISDetectorSetFactory.GenerateDetectorSet(AOwner: TComponent):
                                              IAISPatternSet;
var
  i : Integer;
  DetectorSet : IAISPatternSet;
  Current : IAISPattern;
begin
  DetectorSet :=3D TAISPatternSet.Create(AOwner);

  for i :=3D 0 to FDetectorCount - 1 do
  begin
    Current :=3D DetectorSet.AddPattern;
    Current.Size :=3D FPartitionSize;

    repeat
      InitialiseRandomly(Current);
    until NoMatches(Current);
  end;

  Result :=3D DetectorSet;
end;


procedure TAISDetectorSetFactory.InitialiseRandomly(Pattern: =
IAISPattern);
var
  i : Integer;
begin
  // Totally random rule here for the moment.
  // Maybe we could try a better rule which attempts to better mimic
  // time series data in future??

  for i :=3D 0 to Pattern.Size - 1 do
    Pattern.Items[i] :=3D Random(FBinCount);
end;


function TAISDetectorSetFactory.NoMatches(Pattern: IAISPattern): =
Boolean;
var
  i : Integer;
  Match : Boolean;
begin
  Match :=3D False;

  for i :=3D 0 to SelfSet.Count - 1 do
  begin
    if MatchingRule.Matches(Pattern, SelfSet.Patterns[i]) then
    begin
      Match :=3D True;
      Break;
    end;
  end;

  Result :=3D not Match;
end;

end.
Listing=20 8: Code for IAISDetectorSetFactory interface and=20 TAISDetectorSetFactory component

Change Monitor

The TAISChangeMonitor has a very simple function. It = checks a=20 "monitor set" for matches against a detector set. New data = from our=20 system can be inputted into the change monitor, which will = compare=20 it to our pre-trained detector set. The "Check" function = returns=20 true if a match has been found and false if no match was = found (i.e.=20 the monitor set was found to be "self"). If a match was = found the=20 FirstMatch property will be set to the index of the first = pattern in=20 the monitor set which was matched by the detector set. The=20 MatchCount property gives the number of patterns in the = monitor set=20 which were matched. The CheckFromPattern function can be = used to=20 check the monitor set starting from a given pattern index, = thus=20 CheckFromPattern and FirstMatch can be used to itterate = through the=20 set and find all matching patterns. 

unit UnitAISChangeMonitor;

interface

uses Classes, Dialogs, UnitAISPattern, UnitAISPatternSet, =
UnitAISMatchingRule;

type

  IAISChangeMonitor =3D interface
    function GetMatchingRule: IAISMatchingRule;
    procedure SetMatchingRule(const Value: IAISMatchingRule);
    function GetDetectorSet: IAISPatternSet;
    procedure SetDetectorSet(const Value: IAISPatternSet);
    function GetMatchCount: Integer;
    function GetFirstMatch: Integer;
    function Check(MonitorSet : IAISPatternSet) : Boolean;
    function CheckFromPattern(MonitorSet : IAISPatternSet; P : Integer) =
:
                                                                Boolean;
    property MatchingRule : IAISMatchingRule read GetMatchingRule
                                             write SetMatchingRule;
    property DetectorSet : IAISPatternSet read GetDetectorSet
                                          write SetDetectorSet;
    property MatchCount : Integer read GetMatchCount;
    property FirstMatch : Integer read GetFirstMatch;
  end;

  TAISChangeMonitor =3D class(TComponent, IAISChangeMonitor)
  private
    FMatchingRule : IAISMatchingRule;
    FDetectorSet : IAISPatternSet;
    FMatchCount : Integer;
    FFirstMatch : Integer;
    function GetMatchingRule: IAISMatchingRule;
    procedure SetMatchingRule(const Value: IAISMatchingRule);
    function GetDetectorSet: IAISPatternSet;
    procedure SetDetectorSet(const Value: IAISPatternSet);
    function Match(Pattern : IAISPattern) : Boolean;
    function GetMatchCount: Integer;
    function GetFirstMatch: Integer;
  public
    function Check(MonitorSet : IAISPatternSet) : Boolean;
    function CheckFromPattern(MonitorSet : IAISPatternSet; P : Integer) =
:
                                                                 =
Boolean;
  published
    property MatchingRule : IAISMatchingRule read GetMatchingRule
                                             write SetMatchingRule;
    property DetectorSet : IAISPatternSet read GetDetectorSet
                                          write SetDetectorSet;
    property MatchCount : Integer read GetMatchCount;
    property FirstMatch : Integer read GetFirstMatch;
  end;

procedure Register;

implementation

procedure Register;
begin
  RegisterComponents('AIS Components', [TAISChangeMonitor]);
end;


{ TAISChangeMonitor }

function TAISChangeMonitor.Check(MonitorSet: IAISPatternSet): Boolean;
begin
  Result :=3D CheckFromPattern(MonitorSet, 0)
end;

function TAISChangeMonitor.CheckFromPattern(MonitorSet: IAISPatternSet;
                                                     P: Integer): =
Boolean;
var
  i : Integer;
begin
  Result :=3D False;
  FMatchCount :=3D 0;

  for i :=3D P to MonitorSet.Count - 1 do
  begin
    if Match(MonitorSet.Patterns[i]) then
    begin
      if FMatchCount =3D 0 then
        FFirstMatch :=3D i;
      inc(FMatchCount);
      Result :=3D True;
    end;
  end;
end;

function TAISChangeMonitor.Match(Pattern: IAISPattern): Boolean;
var
  i : Integer;
  Match : Boolean;
begin
  Match :=3D False;

  for i :=3D 0 to DetectorSet.Count - 1 do
  begin
    if MatchingRule.Matches(Pattern, DetectorSet.Patterns[i]) then
    begin
      Match :=3D True;
      Break;
    end;
  end;

  Result :=3D Match;
end;

end.
Listing=20 9: Code for IAISChangeMonitor interface and=20 TAISChangeMonitor component

AIS Test Cases

As the components were written they were continually = tested using=20 the DUnit testing framework. Figure 9 shows some tests being = run.=20 Interestingly, as with many artificial intelligence = techniques,=20 artificial immune systems are stochastic. That is to say = that they=20 are random in nature, in fact they draw their strength from=20 randomness. This makes unit testing a little difficult = though. For=20 example, the test "TestNoIdenticalDetectors" checks that no = two=20 detectors are the same. As you may recall from above, there = is no=20 code in place to stop identical detectors being created, so = we're=20 relying on the fact that it is highly unlikely that = two=20 identical detectors will be created. So the=20 "TestNoIdenticalDetectors" test will pass most of the time, = but fail=20 some of the time, as shown in figure 10.

3D"Figure=20 Figure=20 9: DUnit tests - all successful

Hardened unit testers will no doubt be a little worried = by this=20 inaccuracy, but the nature of stochastic systems prevents us = from=20 ever being sure. The issue of provability has dogged AI for = many=20 years, and its not explained here, except to say: How = can we=20 prove that something is random?

3D"Figure=20 Figure=20 10: DUnitTests with a failure on one of the = "stochastic=20 tests"

To= p=20 of Page

Refrigeration Fault Prediction Demo

The Problem

This demo program shows how we can use our artificial = immune=20 system to detect anomalies in time series data. The data = we'll use=20 is a small snippet of temperature log data recorded from an=20 experimental refrigeration system. Though it by no means = represents=20 the operation of a normal refrigerator, it does give us some = interesting real-world data to play with. Figure 11 shows = the=20 temperature data with time on the X axis (as per usual) and=20 temperature on the Y axis. There are 400 data points which = represent=20 samples of the data at regular intervals (every 15 minutes). = The=20 large spikes correspond to automatic defrosts and are part = of the=20 system's normal behaviour.

3D"Figure=20 Figure=20 11: Fridge temperature data
large version

Figure 12 shows how the fridge data looks before and = after=20 discreetisation. We use 20 symbols here, though we could = easily have=20 used fewer. Note how the discreetised data is greatly = simplified but=20 still manages to represent the general behaviour of the = system. This=20 is exactly what we aim for when choosing the number of = symbols we'll=20 use. Too few symbols would mean loosing the subtleties of = system=20 behaviour, while too many symbols increases the complexity = of the=20 self set and makes detection harder. To make a decision, = sadly, as=20 with so many things in the field of artificial intelligence, = we are=20 forced to combine our intuition with trial and error!

3D"Figure= =20 Figure=20 12: Discreetising the fridge temperature data =
large version

Figure 13 shows (in red) the signal we'll be using to = test that=20 the AIS is capable of detecting faults. The fault, for this = example,=20 is totally synthetic. A sine wave is added to the latter = half of the=20 normal temperature data. This simulates a fault in the = fridge, but=20 does not correspond to the patterns you might see in a real = fridge=20 with a real fault.

3D"Figure=20 Figure=20 13: Normal operation of fridge (blue) and fault = condition=20 (red). Fault starts at time step 200
large version

Figure 14 shows the discreetised plots for the faulty and = normal=20 fridges. Note that, again, a lot of the detail has been lost = but it=20 is still easy to see where the fault starts.

3D"Figure=20 Figure=20 14: Discreetised plot for normal and faulty = temperature=20 data
large version

The Application

The application developed to demonstrate the use of our=20 artificial immune system to detect faults in refrigeration = data is=20 pictured in figure 15. The application is very simple, it = has a big=20 image to display the temperature data graphically and a = number of=20 buttons. The rest if this section gives step by step = instructions on=20 how to use it. The source code, executable and data files = can be=20 downloaded by following the links at the bottom of the page. =

3D"Figure = Figure=20 15: Fridge demo application when first run=20

When the application has loaded, click the "Load Self = Set"=20 button. You will be presented with a load dialog in which = you should=20 select the data file containing the normal = operation=20 dataset ("NormalOperation.mat") and click OK. Next, click = the=20 "Display Self Set" button and the normal operation data = should be=20 displayed as shown in figure 16. The grey lines indicate the = boundaries of the patterns in the dataset.

3D"Figure = Figure=20 16: Fridge demo application with loaded self set=20

Next, create the detector set by clicking the "Create = Detector=20 Set" button. You can look at the detector set by pressing = the=20 "Display Detector Set" button if you like, but it's not very = informative. At any time you can press the "Clear" button to = clear=20 the graph. You can check that the detector set doesn't = detect=20 anything in the self set with which it was trained by = clicking=20 "Check".

Now that the detector set has been created we can load = the fault=20 data. Do this by pressing "Load Self Set" and selecting the=20 appropriate file "WithFault.mat". Click "Clear" followed by = "Display=20 Self Set" to view it. Make sure you don't = click=20 "Create Detector Set" though. We want to keep the detector = set we=20 trained on the normal data. Figure 17 shows the fault data = as=20 displayed by the program.

3D"Figure = Figure=20 17: Fridge demo application with fault data loaded = into=20 self set

Finally we can check the faulty data we loaded into the = self set=20 by clicking "Check". A number of errors should be reported = and the=20 same number of red lines should be displayed on the graph, = as in=20 figure 18. The red lines correspond to patterns which were = matched=20 by the detector set.

3D"Figure = Figure=20 18: Fridge demo application showing patterns = matched by=20 detector set as red lines

Note that the patterns which were matched are all on the = right=20 hand side of the graph. This is because we only introduced = the error=20 signal in the last half of the data set. The first half of = the data=20 set (the left hand side of the graph) is exactly the same as = the=20 normal operation data, so, because our detector set is = guaranteed=20 (by way of the negative selection algorithm) not to match = "self"=20 patterns, the left hand side of the graph is free of red = lines. The=20 second half of the data, however, contains faults which = have,=20 clearly, been detected by our artificial immune system.

In a real world system we might present data from our = test fridge=20 to the detector set as we measure it, continually monitoring = for=20 matches which imply a fault and raising an alarm as = appropriate.=20

Linux Hacker Detection Demo

The second demo is a very simple one, based on the = detection of=20 alterations to configuration files in Linux. Listing 10 = shows part=20 of a standard Linux configuration file "/etc/passwd". This = file=20 contains a list of the users on the system, one per line, = and some=20 configuration information such as their home directory and = user and=20 group IDs. 

root:x:0:0:root:/root:/bin/bash
bin:x:1:1:bin:/bin:/sbin/nologin
daemon:x:2:2:daemon:/sbin:/sbin/nologinologin
...
nscd:x:28:28:NSCD Daemon:/:/bin/false
ident:x:98:98:pident user:/:/sbin/nologin
radvd:x:75:75:radvd user:/:/bin/false
apache:x:48:48:Apache:/var/www:/bin/false
squid:x:23:23::/var/spool/squid:/dev/null
named:x:25:25:Named:/var/named:/bin/false
pcap:x:77:77::/var/arpwatch:/sbin/nologin
mysql:x:27:27:MySQL Server:/var/lib/mysql:/bin/bash
dan:x:500:500:Dan Taylor:/home/dan:/bin/bash
ian:x:502:501:Ian Wallace:/home/ian:/bin/bash
...
Listing=20 10: Part of a pretty normal /etc/passwd file from a = typical=20 Linux system

It is quite possible that a Linux hacker, once they have = gained=20 access to a system, might attempt to add a new user to = /etc/fstab.=20 To test that our artificial immune system can detect = configuration=20 file changes we'll create a copy of the file which we'll = edit in the=20 style of a hacker. The red line in listing 11 shows the = input added=20 by our "evil" hacker. 

...
dan:x:500:500:Dan Taylor:/home/dan:/bin/bash
ian:x:502:501:Ian Wallace:/home/ian:/bin/bash
evil:x:999:501:Evil dummy =
user:/home/dan:/bin/bash
tom:x:503:503:Tom "Lone Axeman" Owen:/home/tom:/bin/bash
ron:x:504:504:The Great Rontana:/home/ron:/bin/bash
...
Listing=20 11: Part of an edited copy of /etc/passwd where a = hacker=20 has added a new user ("evil")

Figure 18 shows the user interface of our Linux hacker = detection=20 example program. The buttons at the bottom so exactly what = the=20 buttons in our previous example did. The white area, in this = program, is a list box which is used to display the = character based=20 patterns.

3D"Figure = Figure=20 19: The hacker detection program

Figure 20 shows the Linux hacker detection program with = the self=20 set loaded. To encode the text file as a list of integers, = in this=20 example, the program simply uses the ordinal (ASCII) value = of each=20 character. This isn't a very good method to use because it = leaves us=20 with a very large symbol set (128 values at least), most of = which we=20 won't ever encounter. A much better technique would be to = use a=20 smaller number of symbols corresponding to the characters = 'a' to 'z'=20 and 'A' to 'Z', the digits '0' to '9' and some useful = punctuation=20 such as '/', '.', '&' etc. We could easily encode all = other=20 characters we encounter as a single integer value, = corresponding to=20 "other".

3D"Figure = Figure=20 20: The hacker detection program showing the self = set=20 (passwd.txt)

Because of the large number of symbols we are using we = are forced=20 to use a much bigger detector set and a much lower matching=20 threshold in order to be able to detect changes. For this = reason the=20 detector set takes a lot longer to create. Figure 21 shows = the=20 detector set. Note that there are a large number of = "garbage"=20 characters in the detector set which could have been avoided = if we'd=20 used a more sensible encoding.

3D"Figure = Figure=20 21: The hacker detection program displaying the = detector=20 set created

We then load the edited file into the self set. Figure 22 = shows=20 the patterns which correspond to the evil user's entry in = the file.=20

3D"Figure = Figure=20 22: The hacker detection program showing the self = set=20 loaded from the edited file - note additions on lines 141 = and 146=20

Figure 23 shows the result of checking the edited self = set. Note=20 that the AIS has managed to detect patterns corresponding to = the=20 correct lines in the file.

3D"Figure = Figure=20 23: The hacker detection program showing the lines = on which=20 matches were found

The Linux hacker detection program is a successful, = though=20 extraordinarily simple demonstration of the ability of = artificial=20 immune systems to detect changes in computer systems. I = could be=20 argued the it would be much easier to simply compare the=20 configuration file to a backup copy and raise an alarm if it = has=20 changed. If we were monitoring only /etc/passwd then this = would be=20 an entirely valid solution. However, there are hundreds of=20 configuration files even on the most basic Linux system = (and, in=20 Windows, the registry contains many thousands of keys). If = we were=20 to watch for changes in these files by comparing them to = backup=20 copies then we would waste a lot of space storing duplicate = files.=20 However, given what we know about artificial immune systems, = it is=20 easy to see that the detector set we would need to monitor=20 all configuration files would be much more compact. = This is=20 because detectors are able to find not just one, but many = possible=20 alterations in the system they monitor.

Conclusions

In this article a simple implementation of an artificial = immune=20 system has been presented. Two example problem areas have = also been=20 explored with promising results. Hopefully the article will = provide=20 a good starting point for Delphi developers who wish to use=20 artificial immune systems in their applications.

Artificial immune systems are a relatively new area of = artificial=20 intelligence research which is extremely promising. A vast = array of=20 unexplored application areas exists, including many problem = areas=20 for which traditional AI techniques (neural networks, = genetic=20 algorithms, expert systems) were not appropriate. The main = strength=20 of the AIS is the fact that it is able to learn without any = negative=20 training examples. This lifts the burden placed on AI = developers to=20 include examples of every possible fault or anomaly = which can=20 occur in their system. Being able to train an anomaly = detection=20 system using only experience of what is "normal" is a huge=20 advantage.

Some other advantages of artificial immune systems are: =

  • Simple training algorithm: learning = involves=20 only the random generation of detectors and the = application of the=20 negative selection algorithm=20
  • "Self taught": no user = intervention=20 is required when creating the detector set=20
  • Simple matching rule: Once a detector = set has=20 been generated the detection of anomalies is very simple. = This=20 means that detection is also computationally inexpensive=20
  • Inherently distributed: Artificial = immune=20 systems are distributed by nature. The generation of the = detector=20 set and subsequent monitoring/ classification can be = performed on=20 different nodes of a distributed system with no = modification to=20 the architecture or computational overhead

Despite their many advantages, artificial immune systems = are by=20 no means perfect. As with all artificial intelligence = techniques=20 they have a number of drawbacks which can not be ignored: =

  • Provability: Artificial immune = systems draw=20 their strength from randomness. The generation of the = detector set=20 is random and, therefore, it is impossible to prove that = an AIS=20 will be able to find every possible novelty or = fault=20 which may occur.=20
  • Parameter setup: A common problem = with AI=20 systems is that the setup of system parameters, such as = the size=20 of self and detector sets and the matching threshold, is = based on=20 guesswork by the developer.=20
  • No unified model: The architecture = presented=20 here is the most popular but my no means the only type of = AIS.=20 Many others exist, details of which can be found in = literature.=20 Until agreement is reached on exactly how an artificial = immune=20 system should work, there will always be the opportunity = for=20 misunderstanding amongst developers.

Though this article covers the basics of artificial = immune=20 systems, it barely scrapes the surface of this extremely = complex=20 field. We conclude with a small list of questions which are = not=20 answered here, in the hope that they will inspire more = research:=20

  • How can we continually adapt our detector set to cope = with=20 changes in the system over time?=20
  • Are there better techniques for generating detectors?=20
  • Would a binary (or similar) encoding cut down the size = of the=20 detector set?=20
  • Can we adjust our detector set to become more = sensitive to=20 particular anomalies?=20
  • Do we need to compare new patterns to every = detector?=20
  • What's the best method for distributing our AIS? =

Acknowledgements

I would like to thank my employers JTL Systems Ltd = for=20 their continued support and for providing some of the data = used in=20 this article. Also, the UK Borland User Group at a meeting of = which this=20 article was first presented (February 2003).

Links and Resources

Downloads

Note that you download and use software and source code = entirely=20 at your own risk. No responsibility will be taken, by = anyone, for=20 any damage of any kind which may or may not be caused by the = use of=20 these files.

That said, please do feel free to download and use the = software,=20 I ask only that I am credited as the original author and = that you e-mail me a = brief outline=20 of your project - simply for the sake of curiosity. Also, if = you=20 make amendments, alterations or upgrades to the code which = you feel=20 are beneficial, please send me a copy so I can update what = is=20 included in this article.

File Description
fridge.zip=20 Fridge data sets
fridgedemo.zip=20 All code for the fridge demo, including code for the = AIS=20 component set
FridgeDemo.exe=20 The fridge demo program, ready to run - though = you'll need=20 the data files too!
linux.zip=20 Linux data sets
linuxdemo.zip=20 All code for the Linux demo, including code for the = AIS=20 component set (again!)
LinuxDemo.exe=20 The Linux demo program (will work with any text = file...)=20

Links

  • Logical Genetics Resources:=20
    • Logical Genetics Paper Database A list = of many=20 academic papers and publications relating to AIS can be = found=20 here, along with any of my associated research notes=20
    • Logical Genetics Forums For your = comments and=20 questions.
  • Artificial Immune Systems:=20
    • "Artificial Immune Systems and their = Applications"=20 This book, edited by Dipankar Dasgupta, is a = must-have for=20 all those who are interested in AIS=20
    • 2nd International Conference on = Artificial Immune Systems To be held in Edinburgh in = September 2003.=20
    • An Overview of the Immune System A = great=20 introduction to the immune system for computer = scientists. From=20 the University of New Mexico.
  • Delphi Stuff:=20
    • Model Maker The CASE tool I used to = create the=20 class hierarchy and diagrams in this article=20
    • DUnit=20 A fantastic unit testing framework which allowed me = to write=20 the code for this article in a very short time=20
=20

"Artificial=20 Immune Systems" (all text and figures) =A9 2003 Dan Taylor, = Logical=20 Genetics

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