Cyber Security and Operations Management for Industry 4.0 1st edition by Ahmed Elngar 1000807290 9781000807295

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ISBN-10 :  1000807290 

ISBN-13 :  9781000807295

Author :  Ahmed A. Elngar 

This book seamlessly connects the topics of Industry 4.0 and cyber security. It discusses the risks and solutions of using cyber security techniques for Industry 4.0. Cyber Security and Operations Management for Industry 4.0 covers the cyber security risks involved in the integration of Industry 4.0 into businesses and highlights the issues and solutions. The book offers the latest theoretical and practical research in the management of cyber security issues common in Industry 4.0 and also discusses the ethical and legal perspectives of incorporating cyber security techniques and applications into the day-to-day functions of an organization. Industrial management topics related to smart factories, operations research, and value chains are also discussed. This book is ideal for industry professionals, researchers, and those in academia who are interested in learning more about how cyber security and Industry 4.0 are related and can work together.

Cyber Security and Operations Management for Industry 4.0 1st Table of contents:

Part 1 Cyber Security
1 Efficient Deep Learning Techniques for Security and Privacy in Industry
1.1 Introduction
1.2 Deep Learning
1.2.1 Deep Learning Techniques
1.2.2 Supervised Learning
1.2.3 Unsupervised Learning
1.2.4 Semi-supervised Learning
1.2.5 Reinforcement Learning
1.3 Deep Learning in Security and Privacy
1.4 Common Cyber Attack Terminology
1.4.1 Spam Detection
1.4.2 Intrusion Detection
1.4.3 Malware Detection
1.5 Deep Learning Security and Privacy Challenges
1.5.1 Model Extraction Attack
1.5.2 Model Inversion Attack
1.5.3 Adversarial Attack
1.5.4 Poisoning Attack
1.6 Other Attacks
1.6.1 Membership Inference Attack
1.6.2 Model Memorization Attack
1.7 Deep Learning Defenses
1.7.1 Trusted Execution Environment
1.8 Ransomware and Extortion Ransomware Attacks
1.9 Conclusion
References
2 Crypto-Based Secure Outbound Supply Chain Authentication for Industry 4.0
2.1 Introduction
2.2 Review of Literature
2.3 The Proposed Methodology
2.3.1 Signature Generation
2.3.2 Numerical Results and Discussions
2.4 Conclusion
2.4.1 Future Scope
References
3 A Machine Learning-Based Approach for Fruit Grading and Classification
3.1 Introduction
3.2 Background
3.3 Methodology
3.3.1 Image Acquisition
3.3.2 Pre-processing
3.4 Multi-Class Support Vector Machine (MSVM)
3.5 Results and Discussion
3.5.1 Performance Analysis
3.5.2 Comparative Analysis
3.6 Conclusions
References
4 Artificial Intelligence Impact on Pattern Classification in Association with IoT for Advanced Applications
4.1 Introduction
4.2 Techniques for Pattern Recognition Using Machine Learning—Overview
4.3 Scope of Pattern Recognition by Deep Learning—Overview
4.4 Times Series Classification Using Pattern Classification
4.5 Case Study Pattern Recognition in Video Surveillance Field
4.6 Conclusions
References
Part 2 Industry 4.0
5 A Modified Clonal Selection Algorithm Based on Positive Selection Method in AIS to Solve the Job-Shop Scheduling Problem
5.1 Introduction
5.2 Job-Shop Scheduling Problem
5.3 Artificial Immune System (AIS)
5.3.1 Positive Selection Theory
5.3.2 Clonal Selection Theory
5.3.3 Affinity Maturation
5.4 Representation of JSSP in AIS
5.4.1 Shape-Space Model of AIS for JSSP
5.4.2 Components in CSA for JSSP Representation
5.4.3 Clonal Selection Algorithm for JSSP
5.4.4 Modification Requirement in the Clonal Selection Algorithm
5.4.5 Realisation of the PSMCSA Algorithm
5.4.6 Generation of an Antigen Library from the PSA
5.5 Conclusion
References
6 IoT Health Care Devices for Patient Monitoring
6.1 Introduction
6.2 Related Work
6.3 Proposed System
6.3.1 Hardware Components and Working Principle
6.3.2 Sensors
6.4 Data Collection
6.5 Results
6.6 Conclusion
References
7 Deep Learning Techniques Used for Detection of Disease in Tomato Plants
7.1 Introduction
7.1.1 Tomato disorders
7.1.2 Problem Statement
7.1.3 Objectives
7.2 Literature Survey
7.3 Methodology
7.3.1 Data Collection
7.3.2 Data Annotation
7.3.3 Faster R-CNN Tomato Disease Detection
7.3.4 RPN Training and Loss Functions
7.4 Experiments and Results with Discussions
7.4.1 Tomato Disease Data Set
7.4.2 Experimental Setup
7.4.3 Quantitative Results
7.5 Qualitative Results
7.6 Conclusion and Future Research

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