Artificial Intelligence and Cyber Security in Industry 4.0

Preface
Contents
1 Introduction to Artificial Intelligence and Cybersecurity for Industry
Introduction
Classification of Cyberattacks (See Fig. 1.1)
Types of Cybersecurity
Tools Used in Cybersecurity
Firewall
Honeypots
How Does Artificial Intelligence Work? (See Fig. 1.3)
AI Implementation Methods
Machine Learning
Deep Learning
Background Information on AI Methods and Cybersecurity Applications
The Significance of Cybersecurity
How AI Can Be Applied on Cybersecurity Issues
AI Techniques Used for Cybersecurity
Security Expert Systems
Deep Learning Detection for Misinformation
Benefits of AI in Cybersecurity
Detecting False Information Using Neural Networks
Using Neural Networks to Find Objectionable YouTube Content
Challenges Faced with Integration of AI in Cybersecurity
Classification Error
Intensive Requirement for Resources
Public Perception
Discussion
Conclusion
References
2 Role of AI and Its Impact on the Development of Cyber Security Applications
Introduction
Overview of Artificial Intelligence
Literature Survey
Artificial Intelligence Techniques for Cyber Security
Various Artificial Intelligence Tools and Techniques Are Mentioned Below
Machine Learning Algorithm Used to Train a Machine
Why AI is Preferred Over Current Anomaly Detection and Prevention Systems?
Use Cases of Artificial Intelligence in Cyber Security
Applications of AI in Cyber Security
AI Solutions for Cyber Security [26]
Limitations of AI in Security
Ethical Issues Related to AI [27]
AI-Based Threat to Cyber Security [27]
Conclusion
References
3 AI and IoT in Manufacturing and Related Security Perspectives for Industry 4.0
Introduction
Role of AI in Manufacturing
Related Works
Technologies
Impact of AI in Manufacturing
Uses Cases of AI in Manufacturing
IoT in Manufacturing
Related Works
Technologies
Need of IIoT Security
Applications
Vulnerabilities and Challenges
Quality Control
Threat Recognition
Configuration of the Hardware
Encryption of Data
Confidentiality of Data
Integration of Cyber-Physical Systems
Devices Pairing Key Establishment
Device Management
Conclusion and Future Work
References
4 IoT Security Vulnerabilities and Defensive Measures in Industry 4.0
Introduction
IoT/IIoT Security Challenges and Requirements
IoT/IIoT Security Challenges
Requirements for IoT/IIoT Security
IoT Architecture and Security Attacks
Architecture of IoT
IoT Security Attacks in Architectural Perspective
Preventive Techniques and Mechanisms of IoT/IIoT Security
Preventive Techniques of IoT Security Attacks
Mechanisms for Combating IoT/IIoT Security Attacks
Conclusion
References
5 Adopting Artificial Intelligence in ITIL for Information Security Management—Way Forward in Industry 4.0
Introduction
Definition of AI
Definition of AI
Application of AI in IT Service Management
Artificial Intelligence for IT Operations
Artificial Intelligence for IT Operations
The Security Technology-Artificial Intelligence Cycle
Operations Understanding Current Use of Artificial Intelligence in Security Technologies
Artificial Intelligence and Its Application to Information Security Management
AI Basics and Early Adopters
Methods AI is Used in Information Security Management
Artificial Intelligence (AI) Security Threats
AI Basics and Early Adopters
Physical Security
Benefits/Areas of AI Applications in Information Security Management
Threat Exposure
Effectiveness Control
Prediction of Breach Risks
Response to Incidences
Explainability
Analysis
Conclusions
References
6 Intelligent Autonomous Drones in Industry 4.0
Introduction
Scope of Study
Design of Drones
Sensor Technologies
Drone Platform
Flight Concept
Reactive Control of Autonomous Drones
Legal Uncertainties
Drone Obstacle Avoidance
Parallelization of Local Path Planning for High-Reliable Autonomous Drones
Short-Range Telemetry Communication for Autonomous Drone Navigation
Autonomous Drone Guidance and Landing System Using AR Markers
Detecting Rogue Drones
Drones Application
Drones in Manufacturing
Autonomous Aerial Counter-Drone System
Internet of Things
Unmanned Aerial Vehicles (UAV) and Its Applications
AI-Based Pipeline Inspection by Drone for Oil and Gas Industry in Bahrain
Disaster Management
Agricultural Drones
Crowd Density Estimation Challenges and Solution
Campus Priority System
Autonomous Drone Control Within a Wi-Fi Network
Improving Image Recognition Accuracy by Contrast Correction in Autonomous Drone Flight
Susceptible Attack Methods Against Autonomous Drones
Conclusion
References
7 A Review on Automatic Generation of Attack Trees and Its Application to Automotive Cybersecurity
Introduction
Automotive Cybersecurity Assurance
Attack Trees
Literature Review Methodology
Review of Attack Tree Generation Methods
Attack Tree Generation in the Automotive Domain
Challenges and New Directions in Attack Tree Generation for the Automotive Domain
Trends in Research
New Directions
Conclusion and Future Research
References
8 Malware Analysis Using Machine Learning Tools and Techniques in IT Industry
Introduction
Malware Analysis Using Machine Learning Techniques
Algorithms for Malware Analysis
Objectives of Malware Analysis
Features in Malware Analysis
Challenges of Malware Analysis
Analysis-Resistant Malware
Accuracy
Case Study
Conclusion
References
9 Use of Machine Learning in Forensics and Computer Security
Introduction
History of Machine Learning
Motivation
The Methods in Machine Learning
Supervised Machine Learning Algorithms
Unsupervised Machine Learning Algorithms
Reinforcement Machine Learning Algorithms
Cyber Threat Intelligence
What Does Threat Intelligence Do?
Who Is a Cyber Threat Intelligence Analyst?
What Does Threat Intelligence Do?
Who Is a Cyber Threat Intelligence Analyst?
Cyber Security
The Domains of Cyber Security and Its Role in Society
Principles of Cyber Security
The Role of ML in Cybersecurity
Importance of Threat Intelligence in Cybersecurity
Security Team Efficiency
Collaborative Knowledge
Top Machine Learning Use Cases for Security
Using Machine Learning to Detect Malicious Activity and Stop Attacks
Using Machine Learning to Analyze Mobile Endpoints
Using Machine Learning to Enhance Human Analysis
Using Machine Learning to Automate Repetitive Security Tasks
Using Machine Learning to Close Zero-Day Vulnerabilities
Hype and Misunderstanding Muddy the Landscape
Applications of Machine Learning in Cybersecurity
Spear Phishing
Watering Hole
Webshell
Ransomware
Remote Exploitation
How Does Machine Learning Benefit Cybersecurity?
Conclusion
Future Scope
References
10 Control of Feed Drives in CNC Machine Tools Using Artificial Immune Adaptive Strategy
Introduction
CNC Machine
Principles of CNC
Principle and Operation of Brushless DC Motor
Mathematical Model of BLDC Motor
Commutation Torque Ripple
Control of Feed Drives
Artificial Immune System
Simulation Study
Comparative Study
Conclusion
References
11 Efficient Anomaly Detection for Empowering Cyber Security by Using Adaptive Deep Learning Model
Introduction
Related Works
Proposed Methodology
Datasets
Data Pre-processing
Classification
Results and Discussion
Conclusion
References
12 Intrusion Detection in IoT-Based Healthcare Using ML and DL Approaches: A Case Study
Introduction
Attacks on IoT-Based Healthcare Ecosystem
Physical Layer
Software/Application Layer
Network Layer
Intrusion Detection in IoT-Based Healthcare
Signature-Based Intrusion Detection System (SIDS)
Anomaly-Based Intrusion Detection System (AIDS)
Classification of AIDS
Ensemble Classifier
AIDS Deployment Techniques in IoT
Centralized Intrusion Detection System
Distributed Intrusion Detection System
Hierarchical Intrusion Detection System
AIDS Validation Techniques
True Positive Rate (TPR)
False Positive Rate (FPR)
False Negative Rate (FNR)
True Negative Rate (TNR)
Accuracy
Confusion Matrix
Conclusion
References
13 War Strategy Algorithm-Based GAN Model for Detecting the Malware Attacks in Modern Digital Age
Introduction
Related Works
Proposed System
Dataset Description
Detection of Malware Using PATE-GAN
Mathematical Model of the War Approach
Results and Discussion
Conclusion
References
14 ML Algorithms for Providing Financial Security in Banking Sectors with the Prediction of Loan Risks
Introduction
Related Work
Methodology
Data Pre-processing
Methodology
Conclusion
References
15 Machine Learning-Based DDoS Attack Detection Using Support Vector Machine
Introduction
Previous Work
Distributed Denial of Service Attack
Application Layer Attack
Protocol Attack
Syn Flood
Volumetric Attacks
SVM
Hyperplane
Support Vectors
Linear SVM
Nonlinear SVM
Deduction of DDoS Through SVM: (SVMBD)
SVM-Based Model Creation
Training the Model
Testing the Model
Assessing the Quality of the Model Using Metrics
Conclusion
References
16 Artificial Intelligence-Based Cyber Security Applications
Introduction
Scope of Study
Understanding Artificial Intelligence
Basic Components of AI
AI for Cyber Security
Traditional System and AI Systems in Cyber Security
AI’s Prevalence and Use in Cyber Security
History of AI in Cyber Security Applications
Gathering of Cyber Security Data for Training Models
Artificial Intelligence for Cyber Security
Expert Systems for Cyber Security
Intelligent Agents
Machine Learning for Cyber Security
Understanding Machine Learning
Machine Learning Algorithms
Machine Learning Methods
Machine Learning Methods for Cyber Threat Prevention
Machine Learning Technology in Cyber Security
Deep Learning for Cyber Security
Choice of Deep Learning Over Machine Learning for Cyber Crime Detection
Deep Learning Methods for Cyber Security
Deep Learning Methods in Cyber Security Applications
AI-Powered Cyber Security Platforms for Enterprises
Performance of an AI Solution
Performance Analysis Metrics of Artificial Intelligence Solutions
Techniques to Enhance the Performance of Artificial Intelligence Solutions
Downsides of Artificial Intelligence in Cyber Security
Credit Card Fraud Detection System
Conclusion and Future Work
References