Deep Learning Approaches for Security Threats in IoT Environments

387 Pages·2022·19.051 MB·English

by  Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash

Deep Learning Approaches forSecurity
Threats inIoT Environments
IEEEPress
445HoesLane
Piscataway,NJ08854
IEEEPressEditorialBoard
SarahSpurgeon,EditorinChief
JónAtliBenediktsson AndreasMolisch DiomidisSpinellis
AnjanBose SaeidNahavandi AhmetMuratTekalp
AdamDrobot JeffreyReed
Peter(Yong)Lian ThomasRobertazzi
Deep Learning Approaches for
Security Threats in IoT
Environments
Mohamed Abdel-Basset
Zagazig University
Egypt
Nour Moustafa
UNSW Canberra at the Australian Defence Force
Academy
Australia
Hossam Hawash
Zagazig University
Egypt
Copyright©2023byTheInstituteofElectricalandElectronicsEngineers,Inc.
Allrightsreserved.
PublishedbyJohnWiley&Sons,Inc.,Hoboken,NewJersey.
PublishedsimultaneouslyinCanada.
Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,or
transmittedinanyformorbyanymeans,electronic,mechanical,photocopying,
recording,scanning,orotherwise,exceptaspermittedunderSection107or108
ofthe1976UnitedStatesCopyrightAct,withouteitherthepriorwritten
permissionofthePublisher,orauthorizationthroughpaymentofthe
appropriateper-copyfeetotheCopyrightClearanceCenter,Inc.,222Rosewood
Drive,Danvers,MA01923,[phone],fax[phone],oronthewebat
www.copyright.com.RequeststothePublisherforpermissionshouldbe
addressedtothePermissionsDepartment,JohnWiley&Sons,Inc.,111River
Street,Hoboken,NJ07030,[phone],fax[phone],oronlineat
http://www.wiley.com/go/permission.
Trademarks:WileyandtheWileylogoaretrademarksorregisteredtrademarks
ofJohnWiley&Sons,Inc.and/oritsaffiliatesintheUnitedStatesandother
countriesandmaynotbeusedwithoutwrittenpermission.Allothertrademarks
arethepropertyoftheirrespectiveowners.JohnWiley&Sons,Inc.isnot
associatedwithanyproductorvendormentionedinthisbook.
LimitofLiability/DisclaimerofWarranty:Whilethepublisherandauthorhave
usedtheirbesteffortsinpreparingthisbook,theymakenorepresentationsor
warrantieswithrespecttotheaccuracyorcompletenessofthecontentsofthis
bookandspecificallydisclaimanyimpliedwarrantiesofmerchantabilityor
fitnessforaparticularpurpose.Nowarrantymaybecreatedorextendedbysales
representativesorwrittensalesmaterials.Theadviceandstrategiescontained
hereinmaynotbesuitableforyoursituation.Youshouldconsultwitha
professionalwhereappropriate.Further,readersshouldbeawarethatwebsites
listedinthisworkmayhavechangedordisappearedbetweenwhenthiswork
waswrittenandwhenitisread.Neitherthepublishernorauthorshallbeliable
foranylossofprofitoranyothercommercialdamages,includingbutnotlimited
tospecial,incidental,consequential,orotherdamages.
Forgeneralinformationonourotherproductsandservicesorfortechnical
support,pleasecontactourCustomerCareDepartmentwithintheUnitedStates
at(800)762-2974,outsidetheUnitedStatesat(317)572-3993orfax(317)
572-4002.
Wileyalsopublishesitsbooksinavarietyofelectronicformats.Somecontent
thatappearsinprintmaynotbeavailableinelectronicformats.Formore
informationaboutWileyproducts,visitourwebsiteatwww.wiley.com.
LibraryofCongressCataloging-in-PublicationData
Names:Abdel-Basset,Mohamed,1985-author.|Moustafa,Nour,author.|
Hawash,Hossam,author.
Title:DeeplearningapproachesforsecuritythreatsinIoTenvironments/
MohamedAbdel-Basset,ZagazigUniversity,Egypt,NourMoustafa,UNSW
CanberraattheAustralianDefenceForceAcademy,Australia,Hossam
Hawash,ZagazigUniversity,Egypt.
Description:Firstedition.|Hoboken,NewJersey:JohnWiley&Sons,
Inc.,[2023]|Includesbibliographicalreferencesandindex.
Identifiers:LCCN2022035671(print)|LCCN2022035672(ebook)|ISBN
9781119884149(hardback)|ISBN9781119884156(adobepdf)|ISBN
9781119884163(epub)
Subjects:LCSH:Internetofthings–Securitymeasures–Dataprocessing.|
Deeplearning(Machinelearning)
Classification:LCCTK5105.8857.A2552023 (print)|LCCTK5105.8857
(ebook)|DDC004.67/8–dc23/eng/20220923
LCrecordavailableathttps://lccn.loc.gov/2022035671
LCebookrecordavailableathttps://lccn.loc.gov/2022035672
CoverDesign:Wiley
CoverImage:©metamorworks/Shutterstock
Setin9.5/12.5ptSTIXTwoTextbyStraive,Pondicherry,India
v
Contents
AbouttheAuthors xv
1 IntroducingDeepLearningforIoTSecurity 1
1.1 Introduction 1
1.2 InternetofThings(IoT)Architecture 1
1.2.1 PhysicalLayer 3
1.2.2 NetworkLayer 4
1.2.3 ApplicationLayer 5
1.3 InternetofThings’VulnerabilitiesandAttacks 6
1.3.1 PassiveAttacks 6
1.3.2 ActiveAttacks 7
1.4 ArtificialIntelligence 11
1.5 DeepLearning 14
1.6 TaxonomyofDeepLearningModels 15
1.6.1 SupervisionCriterion 15
1.6.1.1 SupervisedDeepLearning 15
1.6.1.2 UnsupervisedDeepLearning 17
1.6.1.3 Semi-SupervisedDeepLearning 18
1.6.1.4 DeepReinforcementLearning 19
1.6.2 IncrementalityCriterion 19
1.6.2.1 BatchLearning 20
1.6.2.2 OnlineLearning 21
1.6.3 GeneralizationCriterion 21
1.6.3.1 Model-BasedLearning 22
1.6.3.2 Instance-BasedLearning 22
1.6.4 CentralizationCriterion 22
vi Contents
1.7 SupplementaryMaterials 25
References 25
2 DeepNeuralNetworks 27
2.1 Introduction 27
2.2 FromBiologicalNeuronstoArtificialNeurons 28
2.2.1 BiologicalNeurons 28
2.2.2 ArtificialNeurons 30
2.3 ArtificialNeuralNetwork 31
2.3.1 InputLayer 34
2.3.2 HiddenLayer 34
2.3.3 OutputLayer 34
2.4 ActivationFunctions 35
2.4.1 TypesofActivation 35
2.4.1.1 BinaryStepFunction 35
2.4.1.2 LinearActivationFunction 36
2.4.1.3 NonlinearActivationFunctions 36
2.5 TheLearningProcessofANN 40
2.5.1 ForwardPropagation 41
2.5.2 Backpropagation(GradientDescent) 42
2.6 LossFunctions 49
2.6.1 RegressionLossFunctions 49
2.6.1.1 MeanAbsoluteError(MAE)Loss 50
2.6.1.2 MeanSquaredError(MSE)Loss 50
2.6.1.3 HuberLoss 50
2.6.1.4 MeanBiasError(MBE)Loss 51
2.6.1.5 MeanSquaredLogarithmicError(MSLE) 51
2.6.2 ClassificationLossFunctions 52
2.6.2.1 BinaryCrossEntropy(BCE)Loss 52
2.6.2.2 CategoricalCrossEntropy(CCE)Loss 52
2.6.2.3 HingeLoss 53
2.6.2.4 Kullback–LeiblerDivergence(KL)Loss 53
2.7 SupplementaryMaterials 53
References 54
3 TrainingDeepNeuralNetworks 55
3.1 Introduction 55
3.2 GradientDescentRevisited 56
Contents vii
3.2.1 GradientDescent 56
3.2.2 StochasticGradientDescent 57
3.2.3 Mini-batchGradientDescent 59
3.3 GradientVanishingandExplosion 60
3.4 GradientClipping 61
3.5 ParameterInitialization 62
3.5.1 ZeroInitialization 62
3.5.2 RandomInitialization 63
3.5.3 LecunInitialization 65
3.5.4 XavierInitialization 65
3.5.5 Kaiming(He)Initialization 66
3.6 FasterOptimizers 67
3.6.1 MomentumOptimization 67
3.6.2 NesterovAcceleratedGradient 69
3.6.3 AdaGrad 69
3.6.4 RMSProp 70
3.6.5 AdamOptimizer 70
3.7 ModelTrainingIssues 71
3.7.1 Bias 72
3.7.2 Variance 72
3.7.3 OverfittingIssues 72
3.7.4 UnderfittingIssues 73
3.7.5 ModelCapacity 74
3.8 SupplementaryMaterials 74
References 75
4 EvaluatingDeepNeuralNetworks 77
4.1 Introduction 77
4.2 ValidationDataset 78
4.3 RegularizationMethods 79
4.3.1 EarlyStopping 79
4.3.2 L1andL2Regularization 80
4.3.3 Dropout 81
4.3.4 Max-NormRegularization 82
4.3.5 DataAugmentation 82
4.4 Cross-Validation 83
4.4.1 Hold-OutCross-Validation 84
4.4.2 k-FoldsCross-Validation 85
viii Contents
4.4.3 Stratifiedk-Folds’Cross-Validation 86
4.4.4 Repeatedk-Folds’Cross-Validation 87
4.4.5 Leave-One-OutCross-Validation 88
4.4.6 Leave-p-OutCross-Validation 89
4.4.7 TimeSeriesCross-Validation 90
4.4.8 RollingCross-Validation 90
4.4.9 BlockCross-Validation 90
4.5 PerformanceMetrics 92
4.5.1 RegressionMetrics 92
4.5.1.1 MeanAbsoluteError(MAE) 92
4.5.1.2 RootMeanSquaredError(RMSE) 93
4.5.1.3 CoefficientofDetermination(R2) 93
4.5.1.4 AdjustedR2 94
4.5.2 ClassificationMetrics 94
4.5.2.1 ConfusionMatrix 94
4.5.2.2 Accuracy 96
4.5.2.3 Precision 96
4.5.2.4 Recall 97
4.5.2.5 Precision–RecallCurve 97
4.5.2.6 F1-Score 97
4.5.2.7 BetaF1Score 98
4.5.2.8 FalsePositiveRate(FPR) 98
4.5.2.9 Specificity 99
4.5.2.10 ReceivingOperatingCharacteristics(ROC)Curve 99
4.6 SupplementaryMaterials 99
References 100
5 ConvolutionalNeuralNetworks 103
5.1 Introduction 103
5.2 ShiftfromFullConnectedtoConvolutional 104
5.3 BasicArchitecture 106
5.3.1 TheCross-CorrelationOperation 106
5.3.2 ConvolutionOperation 107
5.3.3 ReceptiveField 108
5.3.4 PaddingandStride 109
5.3.4.1 Padding 109
5.3.4.2 Stride 111
5.4 MultipleChannels 113