Security and Privacy Trends in Cloud Computing and Big Data

It is essential for an organization to know before involving themselves in cloud computing and big data, what are the key security requirements for applications and data processing. Big data and cloud computing are integrated together in practice. Cloud computing offers massive storage, high computation power, and distributed capability to support processing of big data. In such an integrated environment the security and privacy concerns involved in both technologies become combined. This book discusses these security and privacy issues in detail and provides necessary insights into cloud computing and big data integration. It will be useful in enhancing the body of knowledge concerning innovative technologies offered by the research community in the area of cloud computing and big data. Readers can get a better understanding of the basics of cloud computing, big data, and security mitigation techniques to deal with current challenges as well as future research opportunities. Cover Half Title Title Page Copyright Page Table of Contents Preface Editors Contributors 1 Information security framework for cloud and virtualization security 1.1 Introduction 1.2 Virtualization 1.2.1 Type-I hypervisor 1.2.2 Type-II hypervisor 1.2.3 Virtualization benefits 1.2.3.1 Low cost/save energy 1.2.3.2 Small footprints 1.2.3.3 Fast lab provisioning 1.2.3.4 Abstraction 1.2.3.5 Disaster recovery 1.2.3.6 Application isolation 1.2.3.7 Easy migration to cloud 1.2.3.8 Better testing 1.3 Cloud and security issues in virtualization 1.3.1 Virtualization issues 1.3.1.1 Virtualization based malware 1.3.1.2 Mobility 1.3.1.3 Denial of service attack 1.3.1.4 Communication attack among guest VMs and hosts 1.3.1.5 Virtual machine escape 1.3.1.6 Inter-VM attacks and network blind spots 1.4 Information security framework for cloud computing 1.4.1 User network 1.4.2 Security layer 1.4.3 Private cloud 1.4.3.1 User interface layer 1.4.3.2 Platform layer 1.4.3.3 Software layer 1.4.3.4 Management layer 1.4.4 Public cloud 1.5 Conclusion References 2 Security, integrity, and privacy of cloud computing and big data 2.1 Introduction and literature review 2.1.1 What is big data? 2.1.2 Why big data? 2.1.3 Datasets and processing in bigdata 2.2 Big databases 2.2.1 Big data processing 2.2.2 Big data analytics 2.2.3 Big data techniques and visualization 2.3 What is cloud computing? 2.3.1 Cloud services 2.3.2 Cloud deployment models 2.4 Significance of cloud processing for big data 2.5 Challenges and solutions 2.5.1 Challenges: data security and integrity 2.5.2 Data and computing issues and mitigation 2.6 Software issues and mitigation 2.7 Virtualization issues and mitigation 2.8 Web and services issues and mitigation 2.9 Network issues and solutions 2.10 Cloud security issues and solutions 2.11 Authorized data access issues and solutions 2.12 Legal issues and mitigation 2.13 Discussion and open challenges 2.14 Chapter summary References 3 The ways of networks intrusion their detection and prevention 3.1 Introduction 3.2 Literature review 3.3 Methodology 3.3.1 Plan 3.3.1.1 Old work 3.3.1.2 Research 3.3.1.3 Relevant question 3.3.2 Conduction 3.3.2.1 Find out research 3.3.2.2 Output work 3.3.2.3 Extraction 3.3.2.4 Combine data 3.4 Results 3.4.1 Other articles 3.4.2 Report 3.4.2.1 Dynamic assault 3.4.2.2 Mocking 3.4.2.3 Wormhole 3.4.2.4 Creation 3.4.2.5 Repudiation of organizations 3.4.2.6 Sinkhole 3.4.2.7 Sybil 3.4.2.8 Inactive assault 3.4.2.9 Traffic examination 3.4.2.10 Listening 3.4.2.11 Observing 3.4.3 Advance assaults 3.4.3.1 Hustling attack 3.4.3.2 Replay assault 3.4.3.3 Byzantine assault 3.4.3.4 Zone divulgence assault 3.4.4 Security methods 3.4.4.1 Cryptography 3.4.4.2 Firewalls 3.4.4.3 Applications gateway 3.4.4.4 Packet filtering 3.4.4.5 Hybrid systems 3.4.4.6 Development options 3.4.4.7 ASIC machines 3.4.4.8 SSL-VPN 3.4.4.9 Obstruction DPS 3.4.4.10 Wireless security 3.4.4.11 Malware infection 3.5 Discussion 3.6 Conclusion References 4 Cloud-based face recognition for low resource clients 4.1 Introduction: Background 4.2 Related work 4.3 Methodology 4.4 Experiments and results 4.5 Conclusion References 5 Data mining security for big data 5.1 Data mining 5.1.1 Data mining process 5.1.2 Data mining methods 5.1.2.1 Association rules 5.1.2.2 Classification 5.1.2.3 Clustering 5.1.2.4 Decision tree 5.1.2.5 Neural network 5.1.2.6 Regression analysis 5.1.2.7 Statistical analysis 5.1.2.8 Visualization 5.1.3 Data mining for malware detection 5.1.3.1 Approaches of mining for malware detection 5.2 Big data 5.2.1 Types of big data 5.2.1.1 Organized data 5.2.1.2 Unorganized data 5.2.1.3 Semi-organized data 5.2.2 Examples of big data 5.2.2.1 Banking system 5.2.2.2 Education system 5.2.2.3 Media system 5.2.2.4 Healthcare system 5.2.2.5 Agriculture system 5.2.2.6 Travel system 5.2.2.7 Manufacturing system 5.2.2.8 Government system 5.2.2.9 Retail system 5.2.2.10 Energy and utilities system 5.2.2.11 Food industry system 5.3 Security for big data 5.3.1 Component of security of big data 5.3.1.1 Security tools for big data 5.3.1.2 Authentication 5.3.1.3 Authorization 5.3.1.4 Centralized administration and audit 5.3.1.5 Data encryption 5.3.1.6 User access control 5.3.1.7 Physical security 5.3.2 Security issues in big data 5.3.2.1 Access controls 5.3.2.2 Non-relational data stores 5.3.2.3 Storage 5.3.2.4 End points 5.3.2.5 Real time security 5.3.2.6 Threat in data mining 5.4 Conclusion 5.4.1 Future of big data References 6 Cloud computing security challenges and their solutions 6.1 Introduction 6.2 Literature review 6.3 Research methodology 6.3.1 Planning 6.3.1.1 Identification of needs 6.3.1.2 Identification of problem questions 6.3.1.3 Identification of relevant studies 6.3.2 Developing 6.3.2.1 Research identification 6.3.2.2 Selection of papers (exclusion and inclusion) 6.3.2.3 Inclusion criteria 6.3.2.4 Exclusion criteria 6.3.3 Data extraction 6.3.4 Data synthesis 6.3.5 Reporting 6.3.5.1 Finding and formatting the report 6.4 Results and discussion 6.5 Conclusion References 7 Security algorithms for secure cloud environment 7.1 Introduction 7.1.1 History of cloud computing 7.1.2 Cloud computing 7.1.3 Cloud services and models 7.1.4 Cloud service model 7.1.5 Cloud deployment models 7.1.6 Cloud security problems 7.1.7 Goals of security 7.1.8 Types of text 7.1.9 Encryption and decryption method 7.1.10 Keys 7.1.11 Types of security algorithms 7.1.11.1 Symmetric key security algorithms(SKA) 7.1.11.2 Asymmetric key security algorithms (AKA) 7.1.11.3 Types of symmetric key algorithms 7.1.11.4 Types of asymmetric key algorithms 7.2 Literature survey 7.3 Research methodology 7.3.1 Research question 7.3.2 Search strategy 7.3.3 Search phases 7.3.3.1 Survey resources 7.3.3.2 Study selection 7.3.3.3 Inclusion criteria 7.3.3.4 Exclusion criteria 7.3.4 Quality assessment rules 7.4 Results and discussion 7.4.1 Major features of result 7.5 Conclusion References 8 Cloud computing security challenges, analysis of security problems and cloud computing forensics issues 8.1 Introduction 8.2 Literature review 8.3 Data analysis 8.4 Cloud computing architecture and security implications 8.4.1 Infrastructure-as-a-service (IaaS) 8.4.2 Platform-as-a-service (PaaS) 8.4.3 Software-as-a-service (SaaS) 8.5 Trust and security models in cloud computing 8.6 Cloud computing characteristics and security implications 8.7 Cloud computing stakeholders and security implications 8.8 Security and trust model in cloud 8.9 Cloud forensic issues and challenges 8.10 Cloud forensic 8.11 Privacy and pertaining risk in cloud computing 8.12 Cloud forensic investigations 8.13 Issues during forensic investigations involving the cloud 8.14 Summary of solutions presented 8.15 Conclusion 8.16 Future work Further reading 9 Impact of big data in healthcare and management analysis 9.1 Introduction 9.2 Literature review 9.3 Research methodology 9.3.1 Planning the review 9.3.1.1 Performing the review 9.3.1.2 Presenting the review 9.3.2 Research questions 9.3.2.1 SLR protocol 9.3.2.2 Search strategy 9.3.2.3 Search string 9.3.2.4 Selection criteria 9.3.2.5 Study selection process 9.3.2.6 Quality assessment 9.4 Results and discussion 9.4.1 Big Data applications in healthcare sector 9.4.1.1 Healthcare prediction 9.4.1.2 Performance enhancement 9.4.1.3 Determining risk factors 9.4.1.4 Healthcare knowledge system 9.4.1.5 Healthcare management system 9.4.1.6 Big data in oncology 9.4.1.7 Pharmaceuticals 9.4.1.8 Personalized patient care healthcare 9.4.2 Big data: challenges and perspectives 9.4.2.1 Data collection 9.4.2.2 Data processing/analysis of Big Data 9.4.2.3 Economic challenges 9.4.2.4 Data security and privacy 9.4.2.5 Data quality (bad data) 9.4.2.6 Healthcare data quality and interoperability 9.4.2.7 Economic 9.4.3 Management and analysis of Big Data 9.5 Conclusion References 10 Privacy and security issues of big data 10.1 Introduction 10.1.1 Background of big data 10.2 Literature review 10.3 Research methodology 10.3.1 Planning phase 10.3.1.1 Description of the demand for an analysis 10.3.1.2 Determining research questions 10.3.1.3 Recognize the applicable catalog databases 10.3.2 Manage the analysis 10.3.2.1 Instruction choice 10.3.3 Quality assessment 10.3.4 Data mining and synthesize 10.3.5 Reporting the review 10.4 Result and discussion 10.4.1 Discussion 10.5 Conclusion References Index