Mohamed Abdel-Basset, Nour Moustafa, Hossam Hawash

Deep Learning Approaches for Security Threats in Iot Environments

• Gebundenes Buch

Produktbeschreibung

Deep Learning Approaches for Security Threats in IoT Environments

An expert discussion of the application of deep learning methods in the IoT security environment

In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation.

This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues.

Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They'll also find:
_ A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy
_ Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks
_ In-depth examinations of the architectural design of cloud, fog, and edge computing networks
_ Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks

Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity,privacy preservation, and the security of IoT networks.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Wiley & Sons / Wiley-IEEE Press
• Artikelnr. des Verlages: 1W119884140
• 1. Auflage
• Seitenzahl: 384
• Erscheinungstermin: 8. Dezember 2022
• Englisch
• Abmessung: 222mm x 145mm x 24mm
• Gewicht: 535g
• ISBN-13: 9781119884149
• ISBN-10: 1119884144
• Artikelnr.: 63936544

Autorenporträt

Mohamed Abdel-Basset, PhD, is an Associate Professor in the Faculty of Computers and Informatics at Zagazig University, Egypt. He is a Senior Member of the IEEE. Nour Moustafa, PhD, is a Postgraduate Discipline Coordinator (Cyber) and Senior Lecturer in Cybersecurity and Computing at the School of Engineering and Information Technology at the University of New South Wales, UNSW Canberra, Australia. Hossam Hawash is an Assistant Lecturer in the Department of Computer Science, Faculty of Computers and Informatics at Zagazig University, Egypt.

Inhaltsangabe

About the Authors xv

1 Introducing Deep Learning for IoT Security 1

1.1 Introduction 1

1.2 Internet of Things (IoT) Architecture 1

1.2.1 Physical Layer 3

1.2.2 Network Layer 4

1.2.3 Application Layer 5

1.3 Internet of Things' Vulnerabilities and Attacks 6

1.3.1 Passive Attacks 6

1.3.2 Active Attacks 7

1.4 Artificial Intelligence 11

1.5 Deep Learning 14

1.6 Taxonomy of Deep Learning Models 15

1.6.1 Supervision Criterion 15

1.6.1.1 Supervised Deep Learning 15

1.6.1.2 Unsupervised Deep Learning 17

1.6.1.3 Semi-Supervised Deep Learning 18

1.6.1.4 Deep Reinforcement Learning 19

1.6.2 Incrementality Criterion 19

1.6.2.1 Batch Learning 20

1.6.2.2 Online Learning 21

1.6.3 Generalization Criterion 21

1.6.3.1 Model-Based Learning 22

1.6.3.2 Instance-Based Learning 22

1.6.4 Centralization Criterion 22

1.7 Supplementary Materials 25

References 25

2 Deep Neural Networks 27

2.1 Introduction 27

2.2 From Biological Neurons to Artificial Neurons 28

2.2.1 Biological Neurons 28

2.2.2 Artificial Neurons 30

2.3 Artificial Neural Network 31

2.3.1 Input Layer 34

2.3.2 Hidden Layer 34

2.3.3 Output Layer 34

2.4 Activation Functions 35

2.4.1 Types of Activation 35

2.4.1.1 Binary Step Function 35

2.4.1.2 Linear Activation Function 36

2.4.1.3 Nonlinear Activation Functions 36

2.5 The Learning Process of ANN 40

2.5.1 Forward Propagation 41

2.5.2 Backpropagation (Gradient Descent) 42

2.6 Loss Functions 49

2.6.1 Regression Loss Functions 49

2.6.1.1 Mean Absolute Error (MAE) Loss 50

2.6.1.2 Mean Squared Error (MSE) Loss 50

2.6.1.3 Huber Loss 50

2.6.1.4 Mean Bias Error (MBE) Loss 51

2.6.1.5 Mean Squared Logarithmic Error (MSLE) 51

2.6.2 Classification Loss Functions 52

2.6.2.1 Binary Cross Entropy (BCE) Loss 52

2.6.2.2 Categorical Cross Entropy (CCE) Loss 52

2.6.2.3 Hinge Loss 53

2.6.2.4 Kullback-Leibler Divergence (KL) Loss 53

2.7 Supplementary Materials 53

References 54

3 Training Deep Neural Networks 55

3.1 Introduction 55

3.2 Gradient Descent Revisited 56

3.2.1 Gradient Descent 56

3.2.2 Stochastic Gradient Descent 57

3.2.3 Mini-batch Gradient Descent 59

3.3 Gradient Vanishing and Explosion 60

3.4 Gradient Clipping 61

3.5 Parameter Initialization 62

3.5.1 Zero Initialization 62

3.5.2 Random Initialization 63

3.5.3 Lecun Initialization 65

3.5.4 Xavier Initialization 65

3.5.5 Kaiming (He) Initialization 66

3.6 Faster Optimizers 67

3.6.1 Momentum Optimization 67

3.6.2 Nesterov Accelerated Gradient 69

3.6.3 AdaGrad 69

3.6.4 RMSProp 70

3.6.5 Adam Optimizer 70

3.7 Model Training Issues 71

3.7.1 Bias 72

3.7.2 Variance 72

3.7.3 Overfitting Issues 72

3.7.4 Underfitting Issues 73

3.7.5 Model Capacity 74

3.8 Supplementary Materials 74

References 75

4 Evaluating Deep Neural Networks 77

4.1 Introduction 77

4.2 Validation Dataset 78

4.3 Regularization Methods 79

4.3.1 Early Stopping 79

4.3.2 L1 and L2 Regulari