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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:
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.
Mohamed Abdel-Basset, PhD, is an Associate Professor in the Faculty of Computers and Informatics at Zagazig University in Egypt. He is a Senior Member of the IEEE.
Nour Moustafa, PhD, is a Postgraduate Discipline Coordinator (Cyber) and Senior Lecturer in Cyber Security & Computing at the School of Engineering and Information Technology at the University of New South Wales, UNSW Canberra, Australia.
Hossam Hawash is a Research Assistant in the Department of Computer Science, Faculty of Computers and Informatics at Zagazig University in Egypt.
Author Biography
About the Companion Website
1. Chapter 1: INTRODUCING DEEP LEARNING FOR IoT SECURITY
1.1. Introduction
1.2. Internet of Things (IoT) Architectures
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
1.7. Supplementary Materials
2. 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 (ANN)
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. Non-Linear 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
3. 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
3.2.4.
3.3. Gradients vanishing and exploding
3.4. Gradient Clipping
3.5. Parameter initialization
3.5.1. Random initialization
3.5.2. Lecun Initialization
3.5.3. Xavier initialization
3.5.4. 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
4. 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 & 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. Repeated K-folds cross-validation
4.4.4. Leave-one-out cross-validation
4.4.5. Leave-p-out cross-validation
4.4.6. Time series cross-validation
4.4.7. 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 (R-Squared)
4.5.1.4. Adjusted R2
4.5.1.5.
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
5. Chapter 5
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-channels Output
5.4.3. Convolutional kernel 1×1.
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
6. 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-dimension 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
7. 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
8. 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)
8.5.3. Clipping Gradients
8.6. Case study: Malware Detection
8.7. Supplementary Materials
9. 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 Bi-directional Recurrent Network
9.7. Deep Recurrent Network
9.8. Insights
9.9. Case study of Malware Detection
9.10. Supplementary Materials
10. 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
11. 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
12. 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
13. 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
14. Chapter 14: Disentangled Representation GANs
14.1. Introduction
14.2. Disentangled representations
14.3. InfoGAN
14.4. StackedGAN
14.5. Supplementary Materials
15. Chapter 15: Introducing Federated Learning for Internet of Things (IoT)
15.1. Introduction
15.2. Federated Learning in 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. FedML
15.4.3. LEAF
15.4.4. Paddle FL
15.4.5. Federated AI Technology Enabler (FATE)
15.4.6. OpenFL
15.4.7. IBM Federated Learning
15.4.8. NVIDIA FLARE
15.4.9. Flower
15.4.10. Sherpa.ai
15.5. Supplementary Materials
16. Chapter 16: Privacy-Preserved Federated Learning
16.1. Introduction
16.2. Statistical Challenges in Federated Learning
16.2.1. Non-Independent 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
16.2.3. 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
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