Deep Learning Approaches for Security Threats in IoT Environments
- Length: 384 pages
- Edition: 1
- Language: English
- Publisher: Wiley-IEEE Press
- Publication Date: 2022-12-08
- ISBN-10: 1119884144
- ISBN-13: 9781119884149
- Sales Rank: #0 (See Top 100 Books)
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 cyber security 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 undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.
Cover Title Page Copyright Page Contents About the Authors Chapter 1 Introducing Deep Learning for IoT Security 1.1 Introduction 1.2 Internet of Things (IoT) Architecture 1.2.1 Physical Layer 1.2.2 Network Layer 1.2.3 Application Layer 1.3 Internet of Things' Vulnerabilities and Attacks 1.3.1 Passive Attacks 1.3.2 Active Attacks 1.4 Artificial Intelligence 1.5 Deep Learning 1.6 Taxonomy of Deep Learning Models 1.6.1 Supervision Criterion 1.6.1.1 Supervised Deep Learning 1.6.1.2 Unsupervised Deep Learning 1.6.1.3 Semi-Supervised Deep Learning 1.6.1.4 Deep Reinforcement Learning 1.6.2 Incrementality Criterion 1.6.2.1 Batch Learning 1.6.2.2 Online Learning 1.6.3 Generalization Criterion 1.6.3.1 Model-Based Learning 1.6.3.2 Instance-Based Learning 1.6.4 Centralization Criterion 1.7 Supplementary Materials References Chapter 2 Deep Neural Networks 2.1 Introduction 2.2 From Biological Neurons to Artificial Neurons 2.2.1 Biological Neurons 2.2.2 Artificial Neurons 2.3 Artificial Neural Network 2.3.1 Input Layer 2.3.2 Hidden Layer 2.3.3 Output Layer 2.4 Activation Functions 2.4.1 Types of Activation 2.4.1.1 Binary Step Function 2.4.1.2 Linear Activation Function 2.4.1.3 Nonlinear Activation Functions 2.5 The Learning Process of ANN 2.5.1 Forward Propagation 2.5.2 Backpropagation (Gradient Descent) 2.6 Loss Functions 2.6.1 Regression Loss Functions 2.6.1.1 Mean Absolute Error (MAE) Loss 2.6.1.2 Mean Squared Error (MSE) Loss 2.6.1.3 Huber Loss 2.6.1.4 Mean Bias Error (MBE) Loss 2.6.1.5 Mean Squared Logarithmic Error (MSLE) 2.6.2 Classification Loss Functions 2.6.2.1 Binary Cross Entropy (BCE) Loss 2.6.2.2 Categorical Cross Entropy (CCE) Loss 2.6.2.3 Hinge Loss 2.6.2.4 Kullback–Leibler Divergence (KL) Loss 2.7 Supplementary Materials References Chapter 3 Training Deep Neural Networks 3.1 Introduction 3.2 Gradient Descent Revisited 3.2.1 Gradient Descent 3.2.2 Stochastic Gradient Descent 3.2.3 Mini-batch Gradient Descent 3.3 Gradient Vanishing and Explosion 3.4 Gradient Clipping 3.5 Parameter Initialization 3.5.1 Zero Initialization 3.5.2 Random Initialization 3.5.3 Lecun Initialization 3.5.4 Xavier Initialization 3.5.5 Kaiming (He) Initialization 3.6 Faster Optimizers 3.6.1 Momentum Optimization 3.6.2 Nesterov Accelerated Gradient 3.6.3 AdaGrad 3.6.4 RMSProp 3.6.5 Adam Optimizer 3.7 Model Training Issues 3.7.1 Bias 3.7.2 Variance 3.7.3 Overfitting Issues 3.7.4 Underfitting Issues 3.7.5 Model Capacity 3.8 Supplementary Materials References Chapter 4 Evaluating Deep Neural Networks 4.1 Introduction 4.2 Validation Dataset 4.3 Regularization Methods 4.3.1 Early Stopping 4.3.2 L1 and L2 Regularization 4.3.3 Dropout 4.3.4 Max-Norm Regularization 4.3.5 Data Augmentation 4.4 Cross-Validation 4.4.1 Hold-Out Cross-Validation 4.4.2 k-Folds Cross-Validation 4.4.3 Stratified k-Folds' Cross-Validation 4.4.4 Repeated k-Folds' Cross-Validation 4.4.5 Leave-One-Out Cross-Validation 4.4.6 Leave-p-Out Cross-Validation 4.4.7 Time Series Cross-Validation 4.4.8 Rolling Cross-Validation 4.4.9 Block Cross-Validation 4.5 Performance Metrics 4.5.1 Regression Metrics 4.5.1.1 Mean Absolute Error (MAE) 4.5.1.2 Root Mean Squared Error (RMSE) 4.5.1.3 Coefficient of Determination (R2) 4.5.1.4 Adjusted R2 4.5.2 Classification Metrics 4.5.2.1 Confusion Matrix 4.5.2.2 Accuracy 4.5.2.3 Precision 4.5.2.4 Recall 4.5.2.5 Precision–Recall Curve 4.5.2.6 F1-Score 4.5.2.7 Beta F1 Score 4.5.2.8 False Positive Rate (FPR) 4.5.2.9 Specificity 4.5.2.10 Receiving Operating Characteristics (ROC) Curve 4.6 Supplementary Materials References Chapter 5 Convolutional Neural Networks 5.1 Introduction 5.2 Shift from Full Connected to Convolutional 5.3 Basic Architecture 5.3.1 The Cross-Correlation Operation 5.3.2 Convolution Operation 5.3.3 Receptive Field 5.3.4 Padding and Stride 5.3.4.1 Padding 5.3.4.2 Stride 5.4 Multiple Channels 5.4.1 Multi-Channel Inputs 5.4.2 Multi-Channel Output 5.4.3 Convolutional Kernel 1 x 1 5.5 Pooling Layers 5.5.1 Max Pooling 5.5.2 Average Pooling 5.6 Normalization Layers 5.6.1 Batch Normalization 5.6.2 Layer Normalization 5.6.3 Instance Normalization 5.6.4 Group Normalization 5.6.5 Weight Normalization 5.7 Convolutional Neural Networks (LeNet) 5.8 Case Studies 5.8.1 Handwritten Digit Classification (One Channel Input) 5.8.2 Dog vs. Cat Image Classification (Multi-Channel Input) 5.9 Supplementary Materials References Chapter 6 Dive Into Convolutional Neural Networks 6.1 Introduction 6.2 One-Dimensional Convolutional Network 6.2.1 One-Dimensional Convolution 6.2.2 One-Dimensional Pooling 6.3 Three-Dimensional Convolutional Network 6.3.1 Three-Dimensional Convolution 6.3.2 Three-Dimensional Pooling 6.4 Transposed Convolution Layer 6.5 Atrous/Dilated Convolution 6.6 Separable Convolutions 6.6.1 Spatially Separable Convolutions 6.6.2 Depth-wise Separable (DS) Convolutions 6.7 Grouped Convolution 6.8 Shuffled Grouped Convolution 6.9 Supplementary Materials References Chapter 7 Advanced Convolutional Neural Network 7.1 Introduction 7.2 AlexNet 7.3 Block-wise Convolutional Network (VGG) 7.4 Network in Network 7.5 Inception Networks 7.5.1 GoogLeNet 7.5.2 Inception Network v2 (Inception v2) 7.5.3 Inception Network v3 (Inception v3) 7.6 Residual Convolutional Networks 7.7 Dense Convolutional Networks 7.8 Temporal Convolutional Network 7.8.1 One-Dimensional Convolutional Network 7.8.2 Causal and Dilated Convolution 7.8.3 Residual Blocks 7.9 Supplementary Materials References Chapter 8 Introducing Recurrent Neural Networks 8.1 Introduction 8.2 Recurrent Neural Networks 8.2.1 Recurrent Neurons 8.2.2 Memory Cell 8.2.3 Recurrent Neural Network 8.3 Different Categories of RNNs 8.3.1 One-to-One RNN 8.3.2 One-to-Many RNN 8.3.3 Many-to-One RNN 8.3.4 Many-to-Many RNN 8.4 Backpropagation Through Time 8.5 Challenges Facing Simple RNNs 8.5.1 Vanishing Gradient 8.5.2 Exploding Gradient 8.5.2.1 Truncated Backpropagation Through Time (TBPTT) 8.5.2.2 Penalty on the Recurrent Weights Whh 8.5.2.3 Clipping Gradients 8.6 Case Study: Malware Detection 8.7 Supplementary Materials References Chapter 9 Dive Into Recurrent Neural Networks 9.1 Introduction 9.2 Long Short-Term Memory (LSTM) 9.2.1 LSTM Gates 9.2.2 Candidate Memory Cells 9.2.3 Memory Cell 9.2.4 Hidden State 9.3 LSTM with Peephole Connections 9.4 Gated Recurrent Units (GRU) 9.4.1 CRU Cell Gates 9.4.2 Candidate State 9.4.3 Hidden State 9.5 ConvLSTM 9.6 Unidirectional vs. Bidirectional Recurrent Network 9.7 Deep Recurrent Network 9.8 Insights 9.9 Case Study of Malware Detection 9.10 Supplementary Materials References Chapter 10 Attention Neural Networks 10.1 Introduction 10.2 From Biological to Computerized Attention 10.2.1 Biological Attention 10.2.2 Queries, Keys, and Values 10.3 Attention Pooling: Nadaraya–Watson Kernel Regression 10.4 Attention-Scoring Functions 10.4.1 Masked Softmax Operation 10.4.2 Additive Attention (AA) 10.4.3 Scaled Dot-Product Attention 10.5 Multi-Head Attention (MHA) 10.6 Self-Attention Mechanism 10.6.1 Self-Attention (SA) Mechanism 10.6.2 Positional Encoding 10.7 Transformer Network 10.8 Supplementary Materials References Chapter 11 Autoencoder Networks 11.1 Introduction 11.2 Introducing Autoencoders 11.2.1 Definition of Autoencoder 11.2.2 Structural Design 11.3 Convolutional Autoencoder 11.4 Denoising Autoencoder 11.5 Sparse Autoencoders 11.6 Contractive Autoencoders 11.7 Variational Autoencoders 11.8 Case Study 11.9 Supplementary Materials References Chapter 12 Generative Adversarial Networks (GANs) 12.1 Introduction 12.2 Foundation of Generative Adversarial Network 12.3 Deep Convolutional GAN 12.4 Conditional GAN 12.5 Supplementary Materials References Chapter 13 Dive Into Generative Adversarial Networks 13.1 Introduction 13.2 Wasserstein GAN 13.2.1 Distance Functions 13.2.2 Distance Function in GANs 13.2.3 Wasserstein Loss 13.3 Least-Squares GAN (LSGAN) 13.4 Auxiliary Classifier GAN (ACGAN) 13.5 Supplementary Materials References Chapter 14 Disentangled Representation GANs 14.1 Introduction 14.2 Disentangled Representations 14.3 InfoGAN 14.4 StackedGAN 14.5 Supplementary Materials References Chapter 15 Introducing Federated Learning for Internet of Things (IoT) 15.1 Introduction 15.2 Federated Learning in the Internet of Things 15.3 Taxonomic View of Federated Learning 15.3.1 Network Structure 15.3.1.1 Centralized Federated Learning 15.3.1.2 Decentralized Federated Learning 15.3.1.3 Hierarchical Federated Learning 15.3.2 Data Partition 15.3.3 Horizontal Federated Learning 15.3.4 Vertical Federated Learning 15.3.5 Federated Transfer Learning 15.4 Open-Source Frameworks 15.4.1 TensorFlow Federated 15.4.2 PySyft and PyGrid 15.4.3 FedML 15.4.4 LEAF 15.4.5 PaddleFL 15.4.6 Federated AI Technology Enabler (FATE) 15.4.7 OpenFL 15.4.8 IBM Federated Learning 15.4.9 NVIDIA Federated Learning Application Runtime Environment (NVIDIA FLARE) 15.4.10 Flower 15.4.11 Sherpa.ai 15.5 Supplementary Materials References Chapter 16 Privacy-Preserved Federated Learning 16.1 Introduction 16.2 Statistical Challenges in Federated Learning 16.2.1 Nonindependent and Identically Distributed (Non-IID) Data 16.2.1.1 Class Imbalance 16.2.1.2 Distribution Imbalance 16.2.1.3 Size Imbalance 16.2.2 Model Heterogeneity 16.2.2.1 Extracting the Essence of a Subject 16.2.3 Block Cycles 16.3 Security Challenge in Federated Learning 16.3.1 Untargeted Attacks 16.3.2 Targeted Attacks 16.4 Privacy Challenges in Federated Learning 16.4.1 Secure Aggregation 16.4.1.1 Homomorphic Encryption (HE) 16.4.1.2 Secure Multiparty Computation 16.4.1.3 Blockchain 16.4.2 Perturbation Method 16.5 Supplementary Materials References Index EULA
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