Anti-Spam Techniques Based on Artificial Immune System

259 Pages·2015·6.66 MB·English

by  Ying Tan

Anti-Spam Techniques
Based on Artificial
Immune System

Anti-Spam Techniques
Based on Artificial
Immune System
YING TAN
CRC Press
Taylor & Francis Group
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Version Date: 20151013
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Contents
ListofFigures....................................................................xi
ListofTables.................................................................... xv
ListofSymbols ................................................................ xix
Preface.......................................................................... xxi
Acknowledgments .............................................................xxv
Author........................................................................ xxvii
1 Anti-SpamTechnologies..................................................... 1
1.1 SpamProblem............................................................. 1
1.1.1 DefinitionofSpam................................................ 1
1.1.2 ScaleandInfluenceofSpam...................................... 2
1.2 PrevalentAnti-SpamTechnologies........................................ 3
1.2.1 LegalMeans....................................................... 3
1.2.2 E-MailProtocolMethods......................................... 4
1.2.3 SimpleTechniques................................................ 5
1.2.3.1 AddressProtection...................................... 5
1.2.3.2 KeywordsFiltering...................................... 5
1.2.3.3 BlackListandWhite-List............................... 6
1.2.3.4 GrayListandChallenge-Response...................... 6
1.2.4 IntelligentSpamDetectionApproaches.......................... 7
1.3 E-MailFeatureExtractionApproaches................................... 7
1.3.1 TermSelectionStrategies.......................................... 8
1.3.2 Text-BasedFeatureExtractionApproaches....................... 9
1.3.3 Image-BasedFeatureExtractionApproaches.................... 11
1.3.3.1 PropertyFeaturesofImage............................ 11
1.3.3.2 ColorandTextureFeaturesofImage.................. 11
1.3.3.3 CharacterEdgeFeatures............................... 12
1.3.3.4 OCR-BasedFeatures................................... 13
1.3.4 Behavior-BasedFeatureExtractionApproaches................. 13
1.3.4.1 BehaviorFeaturesofSpammers........................ 14
1.3.4.2 NetworkBehaviorFeaturesofSpam................... 15
v
vi ■ Contents
1.3.4.3 SocialNetwork–BasedBehaviorFeatures.............. 15
1.3.4.4 Immune-BasedBehaviorFeatureExtraction
Approaches............................................. 16
1.4 E-MailClassificationTechniques........................................ 17
1.5 PerformanceEvaluationandStandardCorpora......................... 19
1.5.1 PerformanceMeasurements...................................... 19
1.5.2 StandardCorpora................................................ 20
1.6 Summary................................................................. 21
2 ArtificialImmuneSystem.................................................. 23
2.1 Introduction............................................................. 23
2.2 BiologicalImmuneSystem.............................................. 24
2.2.1 Overview......................................................... 24
2.2.2 AdaptiveImmuneProcess....................................... 25
2.2.3 CharacteristicsofBIS............................................ 26
2.3 ArtificialImmuneSystem............................................... 28
2.3.1 Overview......................................................... 28
2.3.2 AISModelsandAlgorithms..................................... 29
2.3.2.1 NegativeSelectionAlgorithm.......................... 30
2.3.2.2 ClonalSelectionAlgorithm............................ 31
2.3.2.3 ImmuneNetworkModel.............................. 33
2.3.2.4 DangerTheoryModel................................. 34
2.3.2.5 ImmuneConcentration................................ 35
2.3.2.6 OtherModelsandAlgorithms......................... 37
2.3.3 CharacteristicsofAIS............................................ 37
2.3.4 ApplicationFieldsofAIS........................................ 38
2.4 ApplicationsofAISinAnti-Spam....................................... 40
2.4.1 HeuristicMethods............................................... 40
2.4.2 NegativeSelection............................................... 41
2.4.3 ImmuneNetwork................................................ 42
2.4.4 DynamicAlgorithms............................................. 42
2.4.5 HybridModels................................................... 43
2.5 Summary................................................................. 44
3 TermSpacePartition-BasedFeatureConstructionApproach............ 45
3.1 Motivation............................................................... 45
3.2 PrinciplesoftheTSPApproach......................................... 47
3.3 ImplementationoftheTSPApproach.................................. 49
3.3.1 Preprocessing..................................................... 49
3.3.2 TermSpacePartition............................................. 49
3.3.3 FeatureConstruction............................................ 51
3.4 Experiments.............................................................. 53
Contents ■ vii
3.4.1 InvestigationofParameters...................................... 53
3.4.2 PerformancewithDifferentFeatureSelectionMetrics.......... 55
3.4.3 ComparisonwithCurrentApproaches.......................... 56
3.5 Summary................................................................. 58
4 ImmuneConcentration-BasedFeatureConstructionApproach......... 59
4.1 Introduction............................................................. 59
4.2 DiversityofDetectorRepresentationinAIS............................ 60
4.3 MotivationofConcentration-BasedFeature
ConstructionApproach.................................................. 61
4.4 OverviewofConcentration-BasedFeature
ConstructionApproach.................................................. 62
4.5 GeneLibraryGeneration................................................ 62
4.6 ConcentrationVectorConstruction..................................... 63
4.7 RelationtoOtherMethods.............................................. 65
4.8 ComplexityAnalysis..................................................... 66
4.9 ExperimentalValidation................................................. 66
4.9.1 ExperimentsonDifferentConcentrations...................... 68
4.9.2 ExperimentswithTwo-ElementConcentrationVector......... 70
4.9.3 ExperimentswithMiddleConcentration....................... 72
4.10 Discussion............................................................... 74
4.11 Summary................................................................. 78
5 LocalConcentration-BasedFeatureExtractionApproach ............... 83
5.1 Introduction............................................................. 83
5.2 StructureofLocalConcentrationModel................................ 84
5.3 TermSelectionandDetectorSetsGeneration........................... 85
5.4 ConstructionofLocalConcentration–BasedFeatureVectors.......... 87
5.5 StrategiesforDefiningLocalAreas...................................... 88
5.5.1 UsingaSlidingWindowwithFixedLength..................... 88
5.5.2 UsingaSlidingWindowwithVariableLength.................. 89
5.6 AnalysisofLocalConcentrationModel................................. 89
5.7 ExperimentalValidation................................................. 90
5.7.1 SelectionofaProperTendencyThreshold...................... 91
5.7.2 SelectionofProperFeatureDimensionality..................... 91
5.7.3 SelectionofaProperSlidingWindowSize...................... 92
5.7.4 SelectionofOptimalTermsPercentage......................... 93
5.7.5 ExperimentsoftheModelwithThreeTerm
SelectionMethods............................................... 93
5.7.6 ComparisonbetweentheLCModeland
CurrentApproaches.............................................. 94
5.7.7 Discussion........................................................ 97
5.8 Summary................................................................. 99
viii ■ Contents
6 Multi-ResolutionConcentration-BasedFeatureConstruction
Approach.................................................................. 101
6.1 Introduction............................................................ 101
6.2 StructureofMulti-ResolutionConcentrationModel.................. 102
6.2.1 DetectorSetsConstruction.................................... 103
6.2.2 CalculationofMulti-ResolutionConcentrations.............. 103
6.3 Multi-ResolutionConcentration-BasedFeatureConstruction
Approach............................................................... 103
6.4 WeightedMulti-ResolutionConcentration-BasedFeature
ConstructionApproach................................................ 105
6.5 ExperimentalValidation................................................ 106
6.5.1 InvestigationofParameters..................................... 107
6.5.2 ComparisonwiththePrevalentApproaches................... 108
6.5.3 PerformancewithOtherClassificationMethods.............. 111
6.6 Summary............................................................... 111
7 AdaptiveConcentrationSelectionModel................................ 115
7.1 OverviewofAdaptiveConcentrationSelectionModel................ 115
7.2 SetupofGeneLibraries................................................ 116
7.3 ConstructionofFeatureVectorsBasedon
ImmuneConcentration................................................ 116
7.4 ImplementationofAdaptiveConcentrationSelectionModel......... 118
7.5 ExperimentalValidation................................................ 119
7.5.1 ExperimentalSetup............................................. 119
7.5.2 ParameterSelection............................................. 120
7.5.3 ExperimentsofProposedModel............................... 122
7.5.4 Discussion...................................................... 123
7.6 Summary............................................................... 124
8 VariableLengthConcentration-BasedFeature
ConstructionMethod..................................................... 125
8.1 Introduction............................................................ 125
8.2 StructureofVariableLengthConcentrationModel................... 126
8.2.1 ConstructionofVariableLengthFeatureVectors.............. 126
8.2.2 RecurrentNeuralNetworks.................................... 127
8.3 ExperimentalParametersandSetup.................................... 129
8.3.1 ProportionofTermsSelection.................................. 129
8.3.2 DimensionofFeatureVectors.................................. 129
8.3.3 SelectionofSizeofSlidingWindow........................... 129
8.3.4 ParametersofRNN............................................ 130
8.4 ExperimentalResultsontheVLCApproach.......................... 131
8.5 Discussion.............................................................. 133
8.6 Summary............................................................... 134