Deep learning approaches for security threats in IoT environments. (2022)

• Record Type: Book
• Further Information: Note: Mohamed Abdel-Basset, Hossam Hawash.
• Authors: Abdel-Basset, Mohamed, 1985-; Moustafa, Nour; Hawash, Hossam
• Contents: 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. &nbs
• Edition: 1st
• Publisher Details: Hoboken : Wiley-IEEE Press
• Publication Date: 2022
• Extent: 1 online resource
• Subjects: 004.678; Internet of things -- Security measures -- Data processing; Deep learning (Machine learning)
• Languages: English
• ISBNs: 9781119884163
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
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