Illumination of Artificial Intelligence in Cybersecurity and Forensics (Lecture Notes on Data Engineering and Communications Technologies, 109)

Preface
Contents
About the Editors
A Practical Experience Applying Security Audit Techniques in An Industrial Healthcare System
	1 Introduction
	2 Background
	3 Security Audit Framework
		3.1 Standardized Frameworks
		3.2 Artificial Intelligence in This Project
	4 Execution of the Security Audit
		4.1 Scope
		4.2 Vulnerability Analysis
		4.3 Vulnerability Exploitation
		4.4 Static Analysis
		4.5 Executive Report
	5 Conclusions and Future Work
	References
Feature Extraction and Artificial Intelligence-Based Intrusion Detection Model for a Secure Internet of Things Networks
	1 Introduction
	2 The Security Issues Within IoT-Based Environments
		2.1 The Following Are Some Examples of Malicious Threats
	3 Applications of Artificial Intelligence for Security and Privacy in Internet of Things Systems
	4 Methods and Materials
		4.1 Particle Swarm Optimization (PSO) Model for Feature Extraction
		4.2 The Convolutional Neural Network Algorithm
		4.3 The CIC-IDS2017 Dataset Characteristics
		4.4 Performance Analysis
	5 Results and Discussion
	6 Conclusion
	References
Intrusion Detection Using Anomaly Detection Algorithm and Snort
	1 Introduction
	2 Background and Literature Review
		2.1 Computer Attacks
		2.2 Need for Intrusion Detection Systems
		2.3 Types of Intrusion Detection Systems.
	3 Methodology
		3.1 Data Collection and Analysis
		3.2 Snort
		3.3 Anomaly Detection in IDS
		3.4 Dataset
		3.5 Monitoring and Data Collation
	4 Presentation and Discussion of Results
		4.1 Implementation
	5 Conclusion and Future Work
	References
Research Perspective on Digital Forensic Tools and Investigation Process
	1 Introduction
	2 Literature Review
	3 Phases of Digital Forensic Investigation
		3.1 Identification Phase
		3.2 Preparation Phase
		3.3 Analysis Phase
		3.4 Documentation Phase
		3.5 Presentation Phase
	4 Digital Forensic Tools
		4.1 Desktop Forensic Tool
		4.2 Network Forensic Tool
		4.3 AI Application in Digital Forensic
		4.4 Live Forensic Tools
		4.5 Operation System Forensic Tools
		4.6 Email Forensic Tool
	5 Challenges and Future Direction of Research in Digital Forensic
		5.1 Challenges
		5.2 Future Direction of Research
	6 Conclusion
	References
Intelligent Authentication Framework for Internet of Medical Things (IoMT)
	1 Introduction
	2 Intelligent Framework
		2.1 Data Collection Modules
		2.2 Biometric Data
		2.3 Pre-processing
	3 Feature Extraction
		3.1 Feature Characteristics
		3.2 Feature Extracted
	4 Evaluation Experimentation
		4.1 Features Evaluation
		4.2 Signal Classification
		4.3 Algorithm Performance
	5 Artificial Intelligent Decision (AIDE) Module
		5.1 Context Awareness Data
		5.2 Artificial Intelligent Authentication
		5.3 The Framework Evaluation
	6 Conclusion
	References
Parallel Faces Recognition Attendance System with Anti-Spoofing Using Convolutional Neural Network
	1 Introduction
	2 Related Works
	3 Attendance System Using CNN
		3.1 Preprocessing Phase
		3.2 Face Detection Phase
		3.3 Anti-Spoofing Phase
		3.4 Decision Phase
		3.5 Proposed Detection Flowchart/Algorithm
	4 Results and Discussion
		4.1 Evaluation Metrics
		4.2 Datasets
		4.3 Performance Evaluation
	5 Conclusion and Future Works
	References
A Systematic Literature Review on Face Morphing Attack Detection (MAD)
	1 Introduction
	2 Previous Related Surveys
	3 Review Method
		3.1 Review Design
		3.2 Review Conduction
	4 Results and Discussion
		4.1 Result
		4.2 Discussion
	5 Parametric Discussion
	6 Taxonomy of MAD Techniques
	7 Open Issues and Future Directions
	8 Conclusion
	Appendix A
	Primary Study in Review
	References
Averaging Dimensionality Reduction and Feature Level Fusion for Post-Processed Morphed Face Image Attack Detection
	1 Introduction
	2 Related Works
	3 Methodology
		3.1 Data Collection
		3.2 Image Sharpening
		3.3 Face Pre-processing
		3.4 Feature Extraction
		3.5 Feature Normalization
		3.6 Averaging Dimensionality Reduction and Feature Fusion
		3.7 Image Classification
		3.8 Performance Metrics
	4 Results and Discussion
	5 Conclusion
	6 Future Works
	References
A Systematic Literature Review  on Forensics in Cloud, IoT, AI & Blockchain
	1 Introduction
	2 Background
	3 Prior Research
	4 Problem Definition
		4.1 Aim
		4.2 Limitations
	5 Systematic Literature Review Process
		5.1 Review Planning
	6 Findings
	7 Results and Discussion
		7.1 Results
		7.2 Discussion
		7.3 How Forensics is Practiced in the Inter-Related, Interdependent Cloud Computing, IoT and Blockchain (ClIoTB) Environment?
		7.4 How Artificial Intelligence Improves the Forensic Methodologies in Digital Forensics and in ClIoTB Environment?
	8 Challenges and Open Issues
		8.1 Cloud Forensics
		8.2 IoT Forensics
		8.3 Blockchain Forensics
		8.4 AI Forensics
	9 Conclusion
	References
Predictive Forensic Based—Characterization of Hidden Elements in Criminal Networks Using Baum-Welch Optimization Technique
	1 Introduction
	2 Literature Review
	3 Methodology
		3.1 Data Collection Technique
		3.2 Characterization of Hidden States
	4 System Model
		4.1 Hidden Markov Model for Criminal States Characterization
		4.2 Terrorist (Network) States
		4.3 Transition Probabilités Matrix
		4.4 Initial Probabilities Matrix
		4.5 Emission Symbols
		4.6 Emission (Observation) Probabilities Matrix
	5 Forensic Predictive Algorithmic Implementation
		5.1 HMM for Criminal AICs and Attacks
	6 Results and Findings
	7 Conclusion and Future Directions
	References
An Integrated IDS Using ICA-Based Feature Selection and SVM Classification Method
	1 Introduction
	2 Literature Review
	3 Material and Method
		3.1 Dataset
		3.2 ML-Based Classifier
	4 Investigational Evaluation
		4.1 Investigational Findings and Discussion
	5 Conclusion and Future Work
	References
A Binary Firefly Algorithm Based Feature Selection Method on High Dimensional Intrusion Detection Data
	1 Introduction
	2 Related Work
	3 Methodology
		3.1 Features Selection
		3.2 Firefly Algorithm
		3.3 Binary Firefly Algorithm
		3.4 Random Forest
	4 Results and Discussion
		4.1 Description of the Dataset
		4.2 Performance Classification of the Proposed BFFA-RF
		4.3 Comparison with the State-of-the-Art
	5 Conclusion and Future Work
	References
Graphical Based Authentication Method Combined with City Block Distance for Electronic Payment System
	1 Introduction
	2 Background and Literature Review
		2.1 Types of Attacks on Passwords
		2.2 Graphical Password Authentication Schemes (GPAS)
		2.3 Categories of Graphical Password Authentication Techniques
		2.4 Performance Metrics
		2.5 Related Works
	3 Methodology
		3.1 Research Design Framework
		3.2 The Equation for the E-Payment Authentication
		3.3 Proposed Graphical Based Authentication Method Combined with City Block Distance
		3.4 The Requirements for the Authentication Method
		3.5 Performance Evaluation
		3.6 Implementation
		3.7 Similarity Measure
	4 Experimental Results
		4.1 Login Success Rate
		4.2 Execution Time
		4.3 Matching Errors for the Distance Measures
		4.4 Analysis of Results
	5 Conclusion and Future Work
	References
Authenticated Encryption to Prevent Cyber-Attacks in Images
	1 Introduction
	2 Key Generation
		2.1 Nonlinear Convolution Using Logistic Map
		2.2 Round Key Generation
	3 Encryption and Decryption
		3.1 Deep Convolutional Generative Adversarial Network Based Test for Special Images
		3.2 Permutations, Merging and Diffusion
		3.3 Authentication
		3.4 Decryption
	4 Performance Analyses
		4.1 Entropy
		4.2 Histogram
		4.3 Keyspace Analysis
		4.4 UACI and NPCR Analysis
		4.5 Known and Chosen Plain Text Attack
		4.6 Correlation
		4.7 Key Sensitivity Analysis
		4.8 Occlusion Attack Analysis
		4.9 Noise Attack Analysis
	5 Conclusions and Future Work
	References
Machine Learning in Automated Detection of Ransomware: Scope, Benefits and Challenges
	1 Introduction
	2 Background Study
	3 Methodology
		3.1 Sources of Research Data
		3.2 Search Key Phrase
		3.3 Search Criteria
		3.4 Data Collection
		3.5 Selection of Relevant Research Works
	4 Results and Discussion
		4.1 Ransomware Detection Models
		4.2 Machine Learning Based Detection Models
		4.3 Architecture of ML Based Ransomware Detection
		4.4 Performance Metrics for ML Based Ransomware Detection Models
		4.5 Datasets for Ransomware Detection Models
	5 Limitations of Machine Learning Based Tools
	6 Conclusion and Future Scope
	References