Table Of ContentIETCOMPUTING SERIES 37
Handbook of Big Data
Analytics
IETBookSeriesonBigData–CallforAuthors
Editor-in-Chief:ProfessorAlbertY.Zomaya,UniversityofSydney,Australia
ThetopicofBigDatahasemergedasarevolutionarythemethatcutsacrossmanytechnologies
andapplicationdomains.Thisnewbookseriesbringstogethertopicswithinthemyriad
researchactivitiesinmanyareasthatanalyse,compute,store,manageandtransportmassive
amountsofdata,suchasalgorithmdesign,dataminingandsearch,processorarchitectures,
databases,infrastructuredevelopment,serviceanddatadiscovery,networkingandmobile
computing,cloudcomputing,high-performancecomputing,privacyandsecurity,storageand
visualization.
Topicsconsideredinclude(butnotrestrictedto)IoTandInternetcomputing;cloudcomputing;
peer-to-peercomputing;autonomiccomputing;datacentrecomputing;multi-coreandmany
corecomputing;parallel,distributedandhigh-performancecomputing;scalabledatabases;
mobilecomputingandsensornetworking;Greencomputing;servicecomputing;networking
infrastructures;cyberinfrastructures;e-Science;smartcities;analyticsanddatamining;BigData
applicationsandmore.
ProposalsforcoherentlyintegratedInternationalco-editedorco-authoredhandbooksand
researchmonographswillbeconsideredforthisbookseries.Eachproposalwillbereviewedby
theEditor-in-Chiefandsomeboardmembers,withadditionalexternalreviewsfrom
independentreviewers.PleaseemailyourbookproposalfortheIETBookSeriesonBigDatato
[email protected]
[email protected].
Handbook of Big Data
Analytics
Volume 2: Applications in ICT, security and
business analytics
Edited by
Vadlamani Ravi and Aswani Kumar Cherukuri
The Institution of Engineering andTechnology
PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom
TheInstitutionofEngineeringandTechnologyisregisteredasaCharityinEngland&
Wales(no.211014)andScotland(no.SC038698).
†TheInstitutionofEngineeringandTechnology2021
Firstpublished2021
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Contents
Abouttheeditors xv
Aboutthecontributors xvii
Foreword xxv
Foreword xxvii
Preface xxix
Acknowledgements xxxi
Introduction xxxiii
1 Big dataanalytics for security intelligence 1
Sumaiya Thaseen Ikram,Aswani KumarCherukuri,
GangLi and Xiao Liu
1.1 Introduction to big data analytics 1
1.2 Big data: huge potentials forinformationsecurity 2
1.3 Big data challenges for cybersecurity 5
1.4 Related work ondecision engine techniques 5
1.5 Big network anomaly detection 7
1.6 Big data forlarge-scale security monitoring 7
1.7 Mechanismstoprevent attacks 10
1.8 Big data analytics for intrusion detection system 12
1.8.1 Challenges of ADS 12
1.8.2 Componentsof ADS 12
1.9 Conclusion 15
Acknowledgment 15
Abbreviations 15
References 16
2 Zero attraction dataselective adaptive filtering algorithm
for bigdataapplications 21
SivashanmugamRadhika and ArumugamChandrasekar
2.1 Introduction 21
2.2 System model 23
2.3 Proposeddata preprocessing framework 24
2.3.1 Proposedupdate rule 26
2.3.2 Selection of thresholds 27
2.3.3 Sparsity model 28
vi Handbook of big data analytics, volume 2
2.4 Simulations 29
2.5 Conclusions 33
References 33
3 Secure routing in software defined networking andInternet of
Thingsfor big data 37
Jayashree Pougajendy, Arun Raj KumarParthibanand Sarath Babu
3.1 Introduction 37
3.2 Architecture of IoT 40
3.3 Intersectionof big data and IoT 41
3.4 Big data analytics 42
3.4.1 Taxonomy of big data analytics 42
3.4.2 Architecture of IoT big data 43
3.5 Security and privacy challenges of big data 45
3.6 Routing protocols in IoT 46
3.7 Security challenges and existing solutions in IoT routing 47
3.7.1 Selective forwarding attacks 47
3.7.2 Sinkhole attacks 47
3.7.3 HELLOflood and acknowledgment spoofingattacks 48
3.7.4 Replay attacks 48
3.7.5 Wormhole attacks 48
3.7.6 Sybil attack 48
3.7.7 Denial-of-service (DoS)attacks 49
3.8 The arrival of SDNinto big data and IoT 49
3.9 Architecture of SDN 50
3.10 Routing in SDN 54
3.11 Attacks onSDNand existing solutions 58
3.11.1 Conflicting flowrules 58
3.11.2 TCAMexhaustion 61
3.11.3 ARPpoisoning 62
3.11.4 Information disclosure 62
3.11.5 Denial-of-service (DoS)attacks 63
3.11.6 Exploiting vulnerabilities in OpenFlow switches 63
3.11.7 Exploiting vulnerabilities in SDNcontrollers 63
3.12 Can SDNbe applied to IoT? 64
3.13 Summary 65
References 66
4 Efficient ciphertext-policy attribute-based signcryption
for secure bigdatastorage in cloud 73
Praveen KumarPremkamal, Syam KumarPasupuleti and Alphonse PJA
4.1 Introduction 74
4.1.1 Related work 75
4.1.2 Contributions 76
Contents vii
4.2 Preliminaries 76
4.2.1 Security model 78
4.3 System model 80
4.3.1 System architecture of ECP-ABSC 80
4.3.2 Formal definitionof ECP-ABSC 81
4.3.3 Security goals 82
4.4 Constructionof ECP-ABSCscheme 82
4.4.1 Setup 82
4.4.2 Key generation 83
4.4.3 Signcrypt 84
4.4.4 Designcrypt 85
4.5 Security analysis 87
4.6 Performance evaluation 94
4.7 Conclusion 99
References 99
5 Privacy-preserving techniquesin bigdata 103
Remya Krishnan Pacheeri andArun Raj KumarParthiban
5.1 Introduction 103
5.2 Big data privacy in data generation phase 105
5.2.1 Access restriction 105
5.2.2 Data falsification 106
5.3 Big data privacy in data storage phase 107
5.3.1 Attribute-based encryption 107
5.3.2 Identity-based encryption 107
5.3.3 Homomorphic encryption 108
5.3.4 Storage path encryption 108
5.3.5 Usage of hybridclouds 109
5.4 Big data privacy in data processing phase 109
5.4.1 Protect data from unauthorized disclosure 109
5.4.2 Extract significant informationwithout trampling privacy 111
5.5 Traditional privacy-preserving techniques and its scalability
in big data 111
5.5.1 Data anonymization 112
5.5.2 Notice and consent 115
5.6 Recent privacy preserving techniques in big data 115
5.6.1 HybrEx 115
5.6.2 Differential privacy 117
5.6.3 Hiding a needle in a haystack: privacy-preserving a priori
algorithm inMapReduce framework 119
5.7 Privacy-preserving solutions in resource constrained devices 121
5.8 Conclusion 122
References 123
viii Handbook of big dataanalytics, volume 2
6 Big dataandbehaviouranalytics 127
Amit KumarTyagi, Keesara Sravanthi and Gillala Rekha
6.1 Introduction about big data and behaviour analytics 128
6.2 Related work 130
6.3 Motivation 133
6.4 Importance and benefits of big data and behaviouranalytics 133
6.4.1 Importance of big data analytics 133
6.5 Existing algorithms, tools available for data analytics and
behaviour analytics 134
6.5.1 Apache Hadoop 135
6.5.2 Cloudera 135
6.5.3 Cassandra 135
6.5.4 Konstanz Information Miner 135
6.5.5 Data wrapper 135
6.5.6 MongoDB 136
6.5.7 HPCC 136
6.6 Open issuesand challengeswith big data analytics and
behaviour analytics 136
6.6.1 Challenges with big data analytics 136
6.6.2 Issueswithbig data analytics (BDA) 137
6.7 Opportunities for future researchers 138
6.8 Ataxonomy for analytics and its related terms 139
6.9 Summary 139
Appendix A 140
References 142
7 Analyzing eventsfor traffic predictiononIoTdatastreams
in asmart city scenario 145
Chittaranjan Hota andSanket Mishra
7.1 Introduction 146
7.2 Related works 148
7.3 Research preliminaries 148
7.3.1 Dataset description 148
7.3.2 Data ingestion 150
7.3.3 Complex event processing 151
7.3.4 Clustering approaches 151
7.3.5 OpenWhisk 152
7.3.6 Evaluationmetrics 153
7.4 Proposed methodology 155
7.4.1 Statistical approach to optimize the number of retrainings 159
7.5 Experimental results and discussion 160
7.6 Conclusion 164
Acknowledgment 165
References 165
Contents ix
8 Gender-basedclassification one-commerce bigdata 169
Chaitanya Kanchibhotla, Venkata LakshmiNarayana
SomayajuluDurvasulaand Radha Krishna Pisipati
8.1 Introduction 170
8.1.1 e-Commerce and big data 171
8.2 Gender prediction methodology 174
8.2.1 Gender prediction based ongender value 174
8.2.2 Classification usingrandom forest 185
8.2.3 Classification usinggradient-boosted trees (GBTs) 188
8.2.4 Experimental results with state-of-the-art classifiers 190
8.3 Summary 194
References 195
9 On recommender systemswithbigdata 197
Lakshmikanth Paleti, P. Radha Krishnaand J.V.R. Murthy
9.1 Introduction 198
9.1.1 Big data and recommender systems 199
9.2 Recommender systems challenges 200
9.2.1 Big-data-specific challenges in RS 202
9.3 Techniques and approaches for recommender systems 204
9.3.1 Early recommender systems 205
9.3.2 Big-data recommender systems 212
9.3.3 X-aware recommender systems 217
9.4 Leveraging big data analytics on recommender systems 218
9.4.1 Healthcare 218
9.4.2 Education 219
9.4.3 Banking and finance 219
9.4.4 Manufacturing 220
9.5 Evaluation metrics 220
9.6 Popular datasets forrecommender systems 221
9.7 Conclusion 223
References 223
10 Analytics in e-commerce at scale 229
Vaidyanathan Subramanianand Arya Ketan
10.1 Background 229
10.2 Analytics use cases 230
10.2.1 Businessand system metrics 230
10.2.2 Data sciences 232
10.3 Data landscape 232
10.3.1 Data producers 233
10.3.2 Data consumers 233
10.3.3 Data freshness 234
10.3.4 Data governance 234
