Secure Data Management for Online Learning Applications

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgements
Editors’ biographies
Contributors
Chapter 1: Secure online assessment of students using optimized deep learning techniques
	1.1 Introduction
	1.2 Literature survey
		1.2.1 Related works
		1.2.2 Review
	1.3 Architectural model
		1.3.1 Proposed architectural model and user-centric system in education
		1.3.2 Support from learners
		1.3.3 Support from teaching
		1.3.4 Centers of learning
		1.3.5 Feedback
	1.4 Process utilized for e-learning
		1.4.1 E-learning trends
		1.4.2 General elements needed for e-learning
			1.4.2.1 Content
			1.4.2.2 Collaboration
			1.4.2.3 Skills management
			1.4.2.4 Assessment
			1.4.2.5 Learning management
			1.4.2.6 Integrated system
	1.5 Cyber security and education
		1.5.1 Building digital trust
		1.5.2 Bring your own device and remote access
		1.5.3 Learning management system security
		1.5.4 Largest cyber security threats in higher education
	1.6 Security threats, detection and protection in distributed e-learning systems
		1.6.1 Cyber security issues
			1.6.1.1 Authentication
			1.6.1.2 Accessibility
			1.6.1.3 Secrecy attacks
			1.6.1.4 Integrity attacks
		1.6.2 Privacy concerns
	1.7 Modified deep neural network by proposed fitness-based butterfly optimization algorithm
		1.7.1 Modified deep neural network
		1.7.2 F-BOA
	1.8 Results and discussions
		1.8.1 Experimental setup
		1.8.2 Learner’s activity ratio analysis
		1.8.3 Performance analysis of learners
		1.8.4 Insecure information analysis
		1.8.5 Accuracy analysis
	1.9 Conclusion
	References
Chapter 2: Survey of risks and threats in online learning applications
	2.1 Introduction
	2.2 Background and related work
	2.3 Risks and threats in online learning
		2.3.1 ARP cache poisoning
		2.3.2 Rootkits
		2.3.3 SQL injection
		2.3.4 Session hijacking
		2.3.5 Credential prediction
		2.3.6 Stack-smashing attacks
		2.3.7 Phishing attacks
	2.4 Discussion
	2.5 Conclusion
	Bibliography
Chapter 3: Approaches to overcome security risks and threats in online learning applications
	3.1 Introduction
	3.2 Security risks and threats
		3.2.1 Broken access control
		3.2.2 Cryptographic failures
		3.2.3 Injection
		3.2.4 Insecure design
		3.2.5 Security misconfigurations
		3.2.6 Vulnerable and outdated components
		3.2.7 Identification and authentication failures
		3.2.8 Software and data integrity failures
		3.2.9 Security logging and monitoring failures
		3.2.10 Server-side request forgery
	3.3 Remedies
		3.3.1 Broken access control
		3.3.2 Cryptographic failures
		3.3.3 Injection
		3.3.4 Insecure design
		3.3.5 Security misconfigurations
		3.3.6 Vulnerable and outdated components
		3.3.7 Identification and authentication failures
		3.3.8 Software and data integrity failures
		3.3.9 Security logging and monitoring failures
		3.3.10 Server-side request forgery
			3.3.10.1 Phishing attacks
		3.3.11 Malware attacks
		3.3.12 DDoS attacks
	3.4 Conclusion
	References
Chapter 4: Secure data aggregation and sharing for online learning applications
	4.1 Introduction
	4.2 Related work
	4.3 Preliminaries
		4.3.1 Trustworthiness evaluation
		4.3.2 Oblivious Random Access Memory (ORAM) and improved ORAM
		4.3.3 Block design and ( v, k + 1, 1)-design
	4.4 Main idea
		4.4.1 Overview of the proposal
		4.4.2 Trustworthiness evaluation based identity authentication
		4.4.3 Privacy-preserving data storage
		4.4.4 Anonymous and traceable data sharing
	4.5 Performance
	4.6 Conclusion
	Acknowledgements
	Note
	References
Chapter 5: A secure data-centric approach to blended learning for programming languages
	5.1 Introduction
		5.1.1 Blended learning
			5.1.1.1 Blended learning models
			5.1.1.2 Advantages and Disadvantages
	5.2 Challenges in teaching programming languages
	5.3 Student-centered learning
	5.4 Learning styles
		5.4.1 Verbal/Linguistic learners
		5.4.2 Visual/Spatial learners
		5.4.3 Auditory/Musical learners
		5.4.4 Physical/Kinesthetic Learners
		5.4.5 Logical/Mathematical learners
		5.4.6 Interpersonal and Intrapersonal learners
	5.5 Strategies used for teaching programming languages
		5.5.1 Talk and chalk approach
		5.5.2 Hands-on programming approach
		5.5.3 Programming tool-based approach
		5.5.4 Storytelling approach
			5.5.4.1 How to create stories
			5.5.4.2 Pen–paper approach
			5.5.4.3 Dig deeper to identify the sole purpose of your story
			5.5.4.4 Use a powerful heading
			5.5.4.5 Design a road map
			5.5.4.6 Conclude with brevity
	5.6 Blended learning platforms
		5.6.1 Live streaming platforms
		5.6.2 Learning management systems
		5.6.3 Recording tools
		5.6.4 Assessment and interaction tools
	5.7 Security in blended learning
	5.8 Summary
	References
Chapter 6: Centralized key distribution protocol using identity-based encryption techniques in cloud computing environments
	6.1 Introduction
		6.1.1 Distributed group communication
		6.1.2 Cloud security workload model
		6.1.3 Mise-en-scène work
	6.2 Literature survey
	6.3 Protocol for identity-based encryption
	6.4 The key generation procedure
		6.4.1 Proposed key distribution process
			6.4.1.1 Member join
			6.4.1.2 Member left
	6.5 Proposed scheme for key generation
		6.5.1 Key updating algorithm
		6.5.2 Security analysis for identity-based encryption
			6.5.2.1 Backward secrecy
			6.5.2.2 Forward secrecy
			6.5.2.3 External user agent
	6.6 Performance analysis of the proposed IBE-based system
	6.7 Conclusion and future work
	References
Chapter 7: Efficient key management and key distribution for online learning
	7.1 Introduction
		7.1.1 Authentication
			7.1.1.1 Message authentication
			7.1.1.2 Connection (access) authentication
		7.1.2 Key generation and distribution
	7.2 Literature review
		7.2.1 E-learning authentication
	7.3 Key encryption techniques for online learning
	7.4 Key management and distribution for online learning
		7.4.1 Key distribution
		7.4.2 Key distribution center (KDC)
			7.4.2.1 Centralized key distribution
			7.4.2.2 Decentralized key distribution
		7.4.3 Key distribution methods
			7.4.3.1 Distribution of public keys
			7.4.3.2 Private key management
			7.4.3.3 Symmetric key distribution using symmetric encryption
	7.5 Cryptographic techniques for secured online learning
	7.6 Performance analysis in the proposed structure
	7.7 Conclusion and future work
	References
Chapter 8: An efficient privacy preserving and public auditing data integrity verification protocol for cloud-based online learning environments
	8.1 Introduction
	8.2 Literature survey
	8.3 The proposed protocol
		8.3.1 System initialization
		8.3.2 Signature generation phase
		8.3.3 Proof request phase
		8.3.4 Proof generation phase
		8.3.5 Proof verification phase
	8.4 Security analysis
		8.4.1 Support for public auditing
		8.4.2 Completeness
		8.4.3 Preservation of data privacy from TPA
	8.5 Analysis of performance
	8.6 Conclusion
	References
Chapter 9: A novel secure e-learning model for accurate recommendations of learning objects
	9.1 Introduction
	9.2 Related works
	9.3 Proposed framework
		9.3.1 Learning style
		9.3.2 Learning object
		9.3.3 Hybrid filtering recommendation
		9.3.4 Secure online evaluation platform
		9.3.5 Formalization of the security model
	9.4 Performance analysis
	9.5 Conclusion
	References
Chapter 10: Efficient key management and key distribution schemes for online learning
	10.1 Introduction
		10.1.1 Standards
		10.1.2 Approaches
		10.1.3 Learning
		10.1.4 Efficient learning approaches
	10.2 Feedback system
	10.3 E-learning with digital educational tools
	10.4 Free e-learning courses
	10.5 Planning and scheduling a course
	10.6 Challenges
	10.7 Tools
	10.8 Online learning – pros and cons
	10.9 Five key success factors for online studying
	10.10 Tips to engage learners for online and hybrid education
	10.11 Six ways to move beyond class work
	10.12 Pandemic with intelligent e-learning requirement
	10.13 Literature survey
	10.14 Conclusions
	References
Chapter 11: Secure virtual learning using blockchain technology
	11.1 Introduction
	11.2 Overview of blockchain technology
	11.3 Five things about blockchain technology
	11.4 Application of blockchain technology in online education
		11.4.1 Full record of learning trajectory
		11.4.2 Certification of academic results that may be trusted
		11.4.3 Educational information is distributed in a distributed mode
	11.5 Academic issues around the world
	11.6 Transition learning using blockchain possibilities
	11.7 Discourse and challenges of blockchain integration in education
	11.8 Various other uses of blockchain technology
		11.8.1 Ethereum
		11.8.2 Health care
		11.8.3 Marketing
		11.8.4 Protecting one’s intellectual property rights
		11.8.5 Applications in community
	11.9 Conclusions and recommendations
	References
Chapter 12: A robust mutual and batch authentication scheme based on ECC for online learning in Industry 4.0
	12.1 Introduction
	12.2 Related works
	12.3 Preliminaries
		12.3.1 Elliptic curve cryptography
		12.3.2 Bilinear pairing
		12.3.3 System overview
	12.4 Proposed scheme
		12.4.1 Initialization phase
		12.4.2 Learner authentication
			12.4.2.1 Learner registration process
			12.4.2.2 Key generation scheme
			12.4.2.3 Learner’s anonymous certificate generation
			12.4.2.4 Learner’s certificate verification
		12.4.3 Instructor’s authentication
			12.4.3.1 Key generation scheme
			12.4.3.2 Instructor’s certificate generation
			12.4.3.3 Instructor’s certificate verification
			12.4.3.4 Integrity verification
			12.4.3.5 License generation
			12.4.3.6 License verification
		12.4.4 Batch authentication
	12.5 Security analysis
		12.5.1 Resistance to impersonation attack
		12.5.2 Resistance to message modification attack
		12.5.3 Resistance to reply attack
		12.5.4 Resistance to fake message attack
		12.5.5 Anonymity and privacy preservation
		12.5.6 Resistance against the non-repudiation attack
	12.6 Performance analysis
		12.6.1 Computational complexity
		12.6.2 Communication cost
		12.6.3 Instructor serving capability
	12.7 Conclusion
	References
Chapter 13: Sentiment Analysis: The beginning
	13.1 Introduction
		13.1.1 Motivation – the decision-making process
		13.1.2 Sources of opinionated text
		13.1.3 Types of sentiment language structure
		13.1.4 Natural language processing
		13.1.5 Sentiment Analysis
			13.1.5.1 Steps in Sentiment Analysis
	13.2 Granularity levels of Sentiment Classification
		13.2.1 Levels of Sentiment Analysis
		13.2.2 Machine learning for Sentiment Analysis
	13.3 Feature extraction and selection techniques
		13.3.1 Term frequency
		13.3.2 Term Presence
		13.3.3 Term position
		13.3.7 Bag of Words
		13.3.8 N-gram features
		13.3.9 Emoticon dictionary
		13.3.10 Topic-based features
		13.3.11 Parts of speech
		13.3.12 Removal of non-alphabetic characters
		13.3.13 Negation handling
		13.3.14 Short form expansion
		13.3.15 Sentiment polarity score
		13.3.16 Number of hashtags
	13.4 Challenges in Sentiment Analysis
	13.5 Research challenge accuracy
	13.6 Applications and scope of Sentiment Analysis
	13.7 Multilingual Sentiment Analysis
		13.7.1 Thamizh text Sentiment Analysis
		13.7.2 Chinese text Sentiment Analysis
		13.7.3 Arabic text Sentiment Analysis
		13.7.4 Urdu text Sentiment Analysis
		13.7.5 Spanish text Sentiment Analysis
	13.8 Performance metrics
	13.9 Conclusion
	References
Index