Artificial Intelligence and Blockchain for Future Cybersecurity Applications

Preface
Contents
About the Editors
Artificial Intelligence and Blockchain for Future Cybersecurity Applications: Architectures and Challenges
Artificial Intelligence and Blockchain for Cybersecurity Applications
	1 Introduction
	2 Cybersecurity and Applications
		2.1 The Scale of Cybersecurity Threat
	3 Blockchain
	4 Artificial Intelligence
	5 Blockchain and Artificial Intelligence Convergence
		5.1 Proposed Model
		5.2 Use Cases for AI and Blockchain Convergence
		5.3 Recommendations for Cybersecurity Applications
	6 Summary
	References
Securing Vehicular Network Using AI and Blockchain-Based Approaches
	1 Introduction
	2 Methodology
	3 Findings and Discussion
		3.1 Problem Addressed
		3.2 Methods
	4 Open Challenges
	5 Conclusion
	References
Privacy-Preserving Multivariant Regression Analysis over Blockchain-Based Encrypted IoMT Data
	1 Introduction
	2 Preliminaries
		2.1 Notations
		2.2 Homomorphic Cryptosystem
		2.3 Blockchain
		2.4 Linear Regression
	3 System Overview
		3.1 System Model
		3.2 Threat Model
		3.3 Encrypted Data Sharing via Blockchain
		3.4 Security Definitions
	4 Model Construction
		4.1 Secure Polynomial Operations (SPO)
		4.2 Secure Comparison (SC)
		4.3 Training Algorithm of Secure Linear Regression
	5 Performance Evaluation
		5.1 Testbed
		5.2 Dataset
		5.3 Float Format Conversion
		5.4 Key Length setting
		5.5 Evaluation parameters
		5.6 Efficiency
	6 Conclusion
	References
Blockchain for Cybersecurity in IoT
	1 Introduction
	2 Background
		2.1 Internet of Things
		2.2 Blockchain
	3 IoT Challenges
		3.1 Privacy
		3.2 Cyber Security
		3.3 Responsibility
		3.4 Energy Consumption in WSNs
	4 Blockchain with IoT
	5 Conclusion
	References
Blockchain and the Future of Securities Exchanges
	1 Introduction
	2 Literature Review
	3 Blockchain and Distributed Ledger Technology
		3.1 Structure of Blockchain-Based Transactions
		3.2 Blockchain and Reduction of Transaction Fees
	4 Platform Economics and Blockchain-Based Securities Markets
		4.1 Network Externalities
		4.2 Routing Rules
	5 Implementation of Blockchain-Based Securities Markets
		5.1 Legal Regimes and Blockchain-Based Securities Markets
		5.2 A Path Forward
	6 Challenges
	7 Conclusion
	References
Artificial Intelligence and Blockchain for Cybersecurity: Applications and Case Studies
Classification of Cyber Security Threats on Mobile Devices and Applications
	1 Introduction
	2 Security in Mobile Devices and Applications
	3 Research Methodology
		3.1 Identifying the Inclusion and Exclusion Criteria
		3.2 Determining the Data Sources and Search Strategies
		3.3 Data Analysis and Coding
		3.4 Classification of Cyber Security Attacks
	4 The Proposed Framework
	5 Conclusion
	References
Revisiting the Approaches, Datasets and Evaluation Parameters to Detect Android Malware: A Comparative Study from State-of-Art
	1 Introduction
	2 The Most Popular Methods for Detecting Android Malware
		2.1 Static Analysis Approach
		2.2 Dynamic Analysis Approach
	3 Methodology
		3.1 Initialization
		3.2 Preprocessing
		3.3 Final Selected Manuscript
		3.4 Extract Information
		3.5 Comparative Analysis
		3.6 Findings
	4 Result and Discussion
		4.1 The Most Applicable Technique
		4.2 The Most Uses Evaluation Parameters
		4.3 Analysis of Algorithms
		4.4 Publisher
		4.5 Dataset
	5 Conclusion
	References
IFIFDroid: Important Features Identification Framework in Android Malware Detection
	1 Introduction
	2 Background Study
	3 IFIFDroid: The Proposed Approach
		3.1 Dataset Description
		3.2 Test Bed Setup
		3.3 Pre-processing
		3.4 Features Extraction
		3.5 Feature Ranking
		3.6 Features Performance Checking
		3.7 Final Selection Based on Performance
	4 Evaluation Parameters and Used Machine Learning Techniques
		4.1 Evaluation Matrices
		4.2 Machine Learning Algorithms
	5 Experimental Results Analysis and Discussion
	6 Conclusion
	References
AntiPhishTuner: Multi-level Approaches Focusing on Optimization by Parameters Tuning in Phishing URLs Detection
	1 Introduction
	2 Literature Review
	3 AntiPhishTuner: Proposed Approach
		3.1 Dataset
		3.2 Feature Description
		3.3 Deep Learning Algorithm
		3.4 Machine Leraning Algorithm
		3.5 Model Generation Phase
	4 Result and Discussion
		4.1 Environment Setup
		4.2 Evaluation Parameters
		4.3 Experiment Result
	5 Conclusion
	References
Improved Secure Intrusion Detection System by User-Defined Socket and Random Forest Classifier
	1 Introduction
		1.1 Intrusion Detection System
		1.2 Types of IDS
		1.3 Random Forest Classifier
	2 Literature Review
	3 Problem Statement
	4 Research Methodology
	5 Tools in Intrusion Detectıon
	6 Proposed Work
	7 Result and Discussion
		7.1 Client Server Setting in Sender and Receiver
		7.2 Sender Implementation
		7.3 Random Forest Implementation
	8 Conclusion
	9 Future Scope
	References
Spark Based Intrusion Detection System Using Practical Swarm Optimization Clustering
	1 Introduction
	2 Preliminaries
		2.1 Particle Swarm Optimization
		2.2 MapReduce Framework
		2.3 Spark Framework
	3 Related Works
	4 Proposed Intrusion Detection System (IDS-SPSO)
		4.1 Pre-processing Phase
		4.2 Data Detector Modeling Phase
		4.3 Evaluation Phase
		4.4 Time Complexity Analysis
	5 Experiments and Results
		5.1 Environment
		5.2 Data Set Description
		5.3 Evaluation Measures
		5.4 Results
	6 Conclusion
	References
A New Scheme for Detecting Malicious Attacks in Wireless Sensor Networks Based on Blockchain Technology
	1 Introduction
		1.1 Research Motivation and Significance
	2 Background of the Study
		2.1 Security Issues in WSNs
		2.2 Overview of Blockchain Technology
		2.3 Applicability of Blockchain in WSNs
		2.4 Research Contribution
	3 Proposed System
		3.1 Overview of Proposed Scheme
		3.2 Detection of Suspected Malicious Nodes Using Heuristic Detection System on CN with Blockchain
		3.3 Detection of Suspected Malicious Nodes Using Signature-Based System on CN with Blockchain
		3.4 Applying the Elimination Decision Formula
	4 Experimentation Analysis and Results
		4.1 Result Analysis of CN Function Based on Heuristic Detection System
		4.2 Result Analysis of CN Function Based on Signature-Based System
		4.3 Result Analysis of CN Function Based on Voting-System for Malicious or Benign Sensor Nodes
		4.4 Result Analysis of CN Functions Based on Heuristic Detection System, Signature-Based System and Voting-System
	5 Conclusion
	References
Artificial Intelligence and Blockchain Applications for Smart Cyber Ecosystems
A Framework Using Artificial Intelligence for Vision-Based Automated Firearm Detection and Reporting in Smart Cities
	1 Introduction
	2 Related Work
	3 Methodology
		3.1 Transfer Learning
		3.2 Model Selection
		3.3 Object Tracking
		3.4 SMS Alert on Weapon Detection
	4 Experimental Evaluations and Results
		4.1 Evaluation Testbed
		4.2 Results and Discussion
	5 Conclusion and Future Work
	References
Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using Deep Learning
	1 Introduction
	2 Related Works
	3 Proposed Contribution
		3.1 Proposed Baseline CNN Architecture
		3.2 Deep Learning Architectures
	4 Experimental Results and Analysis
		4.1 Dataset
		4.2 Data Pre-processing and Splitting
		4.3 Data Augmentation
		4.4 Training and Classification Dataset
		4.5 Experimental Setup
		4.6 Evaluation Criteria
		4.7 Results and Discussion
	5 Conclusions and Future Works
	References
Using Blockchain in Autonomous Vehicles
	1 Introduction
	2 Background
		2.1 Autonomous Vehicles
		2.2 Technologies Used in AV Systems
		2.3 Vehicular ad-hoc Networks (VANETs), Intelligent Transport Systems (ITS) and Connected Vehicles (CVs)
		2.4 Blockchain
		2.5 Scalability with Blockchains
		2.6 Consensus Mechanism
		2.7 Use of Blockchain to Ensure Security
		2.8 Problems and Improvements Associated with AVs
	3 Use of Blockchain in AVs
		3.1 Decentralised Storage and Security Mechanism
		3.2 Blockchain to Improve AV Functionalities
		3.3 Optimizing Related Industries
	4 Analysis
		4.1 Relevance of Blockchain
		4.2 Issues with the Use of Blockchain in AV Systems
		4.3 Future of Related Industries
		4.4 Using Cryptocurrency
		4.5 Resolution of Security Issues
	5 Conclusion
	References
Crime Analysis and Forecasting on Spatio Temporal News Feed Data—An Indian Context
	1 Introduction
	2 Related Work
		2.1 Motivation and Objective of the Research
		2.2 Identifying the Problem Based on Literature
		2.3 List of Crime Keywords Considered
	3 Methodology
		3.1 Implementation of the Process
		3.2 Proposed Analytic Approach
	4 Results and Discussion
		4.1 Geo Spatial Crime Visualization (Hotspot Detection) Using Naïve Bayes and K-Means Algorithms–India
		4.2 Geo Spatial Crime Visualization (Hotspot Detection) Using Naïve Bayes and K-Means Algorithms–Bangalore
		4.3 Geo Spatial Crime Density Analysis Using KDE Algorithm–India and Bangalore
		4.4 Time Series Analysis Using ARIMA Model
	5 Conclusion
	References
Cybersecurity Analysis: Investigating the Data Integrity and Privacy in AWS and Azure Cloud Platforms
	1 Introduction
	2 Literature Review
		2.1 Cloud Computing
		2.2 Infrastructure of Cloud Technology
		2.3 Gaps Analysis in Previous Researches
	3 Theories of Technology Adoption
		3.1 Theory of Reasoned Action (TRA)
		3.2 Technology Acceptance Model (TAM)
		3.3 Expansion of TAM (ETAM)
		3.4 Theory of Planned Behavior (TPB)
		3.5 Model of PC Utilization
		3.6 Motivational Model
		3.7 Unified Theory of Acceptance and Use of Technology (UTAUT)
		3.8 Compatibility UTAUT (C-UTAUT)
		3.9 Diffusion of Innovation (DOI) Theory
		3.10 Social Cognitive Theory
	4 Cloud Computing Service Platforms and Examples
		4.1 Amazon Web Services (AWS)
		4.2 Microsoft Windows Azure
		4.3 Applications and Benefits of Cloud Technology Platforms
	5 Security Patterns in Cloud Computing
		5.1 Security Issues in Cloud Technology
		5.2 Compliance and Regulatory Framework
	6 Data Security and Data Integrity Best Practices and Solutions
		6.1 AWS
		6.2 Windows Azure
	7 Conclusions and Recommendations
	References
Blockchain-Based IoT Forensics: Challenges and State-of-the-Art Frameworks
	1 Introduction
	2 What Is Digital Forensics?
	3 IoT Forensics
		3.1 Characteristics of IoT Environment
		3.2 Type of IoT Forensics
	4 IoT Forensics Data Sources Challenges
	5 Introduction to Blockchain
		5.1 Blockchain
		5.2 Type of Blockchain
	6 Blockchain-Based Framework for IoT Forensics
	7 Conclusion
	References