FAHEEM SYEED MASOODI, PHD, is an Associate Professor of Cybersecurity at Bahrain Polytechnic University. He previously served at the University of Kashmir and the Jazan University in Saudi Arabia. He holds a PhD in Network Security and Cryptography and has published extensively in cryptography, intrusion detection, post-quantum cryptography, financial security, and IoT. His contributions include several books, high-impact papers, and fellowships from France, Brazil, India, and Malaysia.ALWI BAMHDI, PHD, is an Associate Professor in the Computer Sciences Department at Umm ul Qura University, Saudi Arabia. His research interests include mobile ad hoc networks, wireless sensor networks, and information security.

• About the Editors xixList of Contributors xxiForeword xxvPreface xxviiAcknowledgments xxix1 Intrusion Detection in the Age of Deep Learning: An Introduction 1Faheem Syeed Masoodi1.1 Introduction 11.1.1 The Pioneers of Network Security 21.1.1.1 Limitations of the Existing System 21.1.2 How Firewalls Are Different from IDS 31.1.3 Need for Intrusion Detection Systems 41.1.4 Intrusion Detection System 51.1.4.1 Intrusion Detection Technologies 91.1.4.2 Intrusion Detection Methodologies 141.1.4.3 Intrusion Detection Approaches 171.1.5 Need for Deep Learning Based IDS 21References 222 Machine Learning for Intrusion Detection 25Divya M.K.2.1 Introduction 252.1.1 Overview of Intrusion Detection Systems (IDSs) 252.1.1.1 Types of IDSs: Host-Based, Network-Based, Hybrid 262.2 Role of Machine Learning in IDSs 292.2.1 Benefits and Challenges of Using Machine Learning in IDSs 292.2.1.1 Benefits of ML in IDSs 292.2.1.2 Challenges of ML in IDS 292.2.2 Evolution from Traditional Methods to ML-Based Approaches in IDSs 302.2.2.1 Traditional Methods in IDSs 302.2.2.2 Transition to ML-Based Approaches 312.2.2.3 Current ML-Based IDS Landscape 312.3 Fundamentals of Machine Learning 322.3.1 Key ML Techniques 322.3.1.1 How These Concepts Enable Pattern and Anomaly Detection 332.3.2 Key Algorithms Used in Intrusion Detection 332.3.3 Classification Algorithms 332.3.3.1 Clustering Algorithms 342.3.3.2 Anomaly Detection Algorithms 352.4 Data Preparation for IDSs 352.4.1 Types of Data Used in IDSs 362.4.2 Data Preprocessing Techniques 372.5 Supervised Learning for Intrusion Detection 372.5.1 Key Components of Supervised Learning 372.5.2 Benefits of Supervised Learning in IDSs 382.5.3 Challenges of Supervised Learning in IDSs 382.5.4 Common Supervised Learning Techniques in IDSs 392.5.5 Supervised Learning Algorithms 392.5.6 Practical Example: Using Supervised Learning in IDSs 412.6 Unsupervised Learning for Intrusion Detection Systems (IDSs) 412.6.1 Techniques and Algorithms 432.6.2 Example Use Case: Anomaly-Based Network Intrusion Detection 442.7 Semi-Supervised Learning in Intrusion Detection Systems (IDSs) 442.7.1 Semi-Supervised Algorithms and Applications 462.7.2 Applications in IDSs 482.7.3 Example Use Case: Semi-Supervised Network Intrusion Detection 492.8 Reinforcement Learning for Intrusion Detection System 492.8.1 Example Scenario 512.9 Feature Engineering, Model Training, and Hyperparameter Tuning in Ids 532.9.1 Feature Engineering in IDS 532.9.2 Model Training in IDS 542.9.3 Hyperparameter Tuning in IDSs 552.9.4 Practical Implementation Challenges in IDSs 56References 563 Deep Learning Fundamentals-I 59Razeef Mohd and Abeena Mohiudin Azad3.1 Introduction to Deep Learning 593.1.1 Definition and Importance 593.1.2 Deep Learning in Cybersecurity: Enhancing Threat Detection and Prevention 613.1.3 Key Areas Where Deep Learning Enhances Cybersecurity 613.1.3.1 Proactive Threat Detection with Deep Learning 623.2 Conceptual Foundations of Deep Learning 633.2.1 Historical Evolution of Deep Learning 633.2.2 Key Differences Between Deep Learning and Traditional Machine Learning 643.2.3 Why Deep Learning Is Suited for Intrusion Detection 643.2.4 Artificial Neural Networks (ANNs) as the Core of Deep Learning 653.2.4.1 Structure of ANNs 653.2.4.2 Working Mechanism of ANNs 653.2.4.3 The Role of Deep Learning in Pattern Recognition and Anomaly Detection 663.3 Neural Networks: The Building Blocks of Deep Learning 663.3.1 Biological Inspiration and Mathematical Representation 663.3.2 Architecture of Neural Networks (Layers, Activation Functions, and Weights) 673.3.2.1 Layers in Neural Networks 673.3.2.2 Neuron Activation Function 683.3.2.3 Types of Activation Functions 683.3.3 Training Deep Learning Models Using Backpropagation and Weight Optimization 693.3.3.1 Error Functions in Neural Networks 703.3.3.2 Steps in Backpropagation 703.3.4 Gradient Descent: The Backbone of Learning in Neural Networks 713.3.4.1 Advanced Optimization Techniques 723.3.5 Regularization Techniques in Neural Networks 733.3.5.1 L1 and L2 Regularization 733.3.6 Dropout: Reducing Overfitting 733.3.6.1 Impact of Activation Functions and Optimization on Deep Learning 743.4 Applications of Deep Learning in Intrusion Detection 753.4.1 Types of Cyber Threats and Attacks 753.4.1.1 DDoS Attacks 753.4.1.2 Malware and Ransomware 753.4.1.3 Brute Force Attacks 753.4.1.4 Insider Threats 763.4.2 Deep Learning-Based Intrusion Detection Systems (IDSs) 763.4.2.1 Signature-Based IDS 763.4.2.2 Anomaly-Based IDS 763.4.2.3 Deep Learning Models Commonly Used for IDSs 773.4.3 Case Studies and Real-World Implementations 773.4.3.1 Financial Institutions 773.4.3.2 Technology Companies 783.4.3.3 Healthcare Organizations 783.4.3.4 Government Agencies 783.4.3.5 Retail and E-Commerce 783.5 Security-Enhancing Potential of Deep Learning 793.5.1 Advantages of Deep Learning in Cybersecurity 793.5.1.1 Automated Threat Detection 793.5.1.2 High Accuracy 793.5.1.3 Scalability 803.5.1.4 Adaptability to Evolving Threats 803.5.1.5 Reduced False Positives 803.5.2 Challenges and Limitations of Deep Learning-Based IDS 803.5.2.1 Computational Costs 813.5.2.2 Adversarial Attacks 813.5.2.3 Data Availability and Quality 813.5.3 Future Directions in AI-Driven Intrusion Detection 823.5.3.1 Federated Learning 823.5.3.2 Explainable AI (XAI) 823.5.3.3 Integration with Blockchain 823.5.3.4 Continuous Learning and Adaptation 833.6 Conclusion 833.6.1 Summary of Key Insights 833.6.2 Future Directions in Deep Learning for Cybersecurity 84References 844 Deep Learning Fundamentals-II 91Saduf Afzal, Shifaa Basharat, and Shozab Khurshid4.1 Introduction 914.2 Artificial Neural Networks 924.3 Overview of Deep Learning 944.4 Deep Learning Algorithms 954.4.1 Deep Neural Networks (DNNs) 954.4.2 Deep Belief Networks 964.4.3 Autoencoders 974.4.4 Convolutional Neural Network 984.4.5 Recurrent Neural Networks 994.5 Conclusion 102References 1025 Intrusion Detection Through Deep Learning: Emerging Trends and Challenges 107Achyutananda Mishra5.1 Introduction 1075.2 Deep Learning 1085.2.1 Neural Network Architectures 1095.2.2 Types of Neural Networks 1105.2.2.1 Feed-forward Neural Networks (FNNs) 1105.2.2.2 Convolutional Neural Networks (CNNs) 1115.2.2.3 Recurrent Neural Networks (RNNs) 1115.2.2.4 Recursive Neural Networks (RvNNs) 1125.3 Applications of Deep Learning 1125.4 Intrusion Detection 1135.4.1 Classification 1165.5 Methodologies of Detection 1165.6 Deep Learning for Intrusion Detection 1175.7 Limitations 1195.7.1 Mr. William’s Case 1195.7.2 Challenges 1205.8 Conclusion 120References 1216 Dataset for Evaluating Deep Learning-Based Intrusion Detection 125Wasia Ashraf, Faheem Syeed Masoodi, and Asra Khanam6.1 Introduction 1256.2 Data 1266.2.1 Packet-Based Data 1266.2.2 Flow-Based Data 1276.2.3 Other Data 1276.3 Dataset Properties 1286.3.1 Basic Information 1286.3.2 Nature of Data 1296.3.3 Data Volume 1296.3.4 Recording Environment 1296.3.5 Evaluation 1306.4 Datasets 1316.4.1 Darpa 1316.4.2 Kdd 1999 1336.4.3 Nsl-kdd 1346.4.4 Iscx- 2012 1376.4.5 Unsw-nb 15 1396.4.6 Cic-ids- 2017 1416.5 Conclusion 143References 1447 Deep Learning Features: Techniques for Extraction and Selection 147K.S. Shashikala, Sneha Shinde, Sandyarani Vadlamudi, and Mahendra Shridhar Naik7.1 Introduction 1477.1.1 Overview of Intrusion Detection Systems (IDSs) 1477.1.2 Role of Deep Learning in IDSs 1487.1.3 Importance of Feature Extraction and Selection 1497.1.3.1 Feature Extraction 1497.1.3.2 Feature Selection 1497.1.3.3 Critical Role in IDSs 1507.1.4 Improvement in Accuracy, Complexity Reduction, and Efficiency Enhancement 1507.1.5 Challenges in Managing High-Dimensional Data in IDSs 1527.2 Techniques for Feature Extraction and Selection 1537.2.1 Principal Component Analysis 1537.2.2 Linear Discriminant Analysis 1537.2.3 Mutual Information 1547.2.3.1 How Mutual Information Works? 1547.2.4 Chi-Squared Feature Selection 1557.2.4.1 How Chi-Squared Feature Selection Works? 1557.2.5 Comparative Analysis of Techniques 1567.3 Applications in Intrusion Detection Systems 1587.3.1 Integrating Feature Extraction and Selection in IDS Workflows 1587.3.1.1 Impact on Performance 1597.3.1.2 Challenges in Real-World Applications 1597.3.2 Performance Improvements 1597.3.2.1 Efficiency Gains Through MI and Chi-Squared Methods 1597.3.2.2 Enhancing Scalability for Growing Network Demands 1607.3.3 Practical Deployment 1607.3.3.1 Preprocessing with PCA and LDA 1607.3.3.2 Training with MI and Chi-Squared Methods 1617.3.3.3 Hybrid Approaches for Enhanced Results 1617.3.3.4 Real-World Applications 1617.4 Conclusion and Future Trends 1627.4.1 Key Insights 1627.4.2 Future Directions 163References 1648 Exploring Advanced Artificial Intelligence for Anomaly Detection 167Palanisamy Padmaloshani8.1 Introduction 1678.1.1 Types of Anomalous Detection 1678.1.2 Artificial Intelligence-Based Anomaly Detection 1688.1.2.1 AI-Based AD Process 1688.1.2.2 Machine Learning Algorithms for AD 1688.1.2.3 Application Domains 1698.1.2.4 Advantages of AI-Based AD Methods 1708.1.2.5 Challenges in AI-Based AD 1708.1.2.6 AI-Based AD Methods 1708.2 Autoencoder-Based Anomaly Detection 1718.2.1 Types of Autoencoders 1728.3 Generative Adversarial Networks Anomaly Detection 1738.3.1 Features of GANs 1738.3.2 Working Principle of GANs 1748.4 One-Class Classification Anomaly Detection 1758.5 Deep Reinforcement Learning Anomaly Detection 1778.6 Recurrent Neural Networks-Based Anomaly Detection 1788.7 Transfer Learning Anomaly Detection 1798.8 Conclusion 181References 1819 Enhancing Security in Smart Environments Using Deep Learning: A Comprehensive Approach 185Syed Irfan Yaqoob, Preet Kamal, Shivani Aggarwal, Anuradha Kanade, and Shantanu Kanade9.1 Introduction 1859.1.1 Understanding Smart Environments and Their Security Needs 1879.1.2 Connectivity: The Backbone of Smart Environments 1879.2 Automation: Autonomous Decision-Making for Efficiency 1889.3 Data Collection and Analytics: Leveraging Big Data for Optimization 1899.4 Data Privacy and Integrity 1909.5 Authentication and Access Control 1919.6 Intrusion Detection 1929.7 Adaptability to Evolving Threats 1939.8 The Role of Deep Learning in Security Enhancement 1949.8.1 Anomaly Detection with Deep Learning 1949.8.1.1 Unsupervised Learning: Detecting Novel Security Threats 1949.8.1.2 High-dimensional Data Processing: Learning Complex Patterns in Smart Environments 1959.8.2 Real-Time Analysis: Continuous Monitoring and Threat Detection 1969.8.3 Intrusion Detection Systems (IDSs) Using Deep Learning 1979.8.4 Adaptive Defense Mechanisms 1989.8.4.1 Self-learning Systems: Evolving with New Data 1989.8.4.2 Threat Prediction and Prevention: Anticipating Attacks Before They Happen 1999.8.4.3 Automated Response: Mitigating Threats Instantly 2009.8.4.4 Interdisciplinary Collaboration for Enhanced Security 2019.8.5 Cybersecurity and Machine Learning Experts: Designing Security Solutions for Smart Environments 2019.8.6 Device Manufacturers and IoT Developers: Building Security into Hardware and Software 2029.8.7 Ethics and Privacy Experts: Ensuring User Privacy and Trust 2039.8.8 Policymakers and Regulators: Establishing Standards and Regulations 2049.9 Challenges and Future Directions 2049.10 Conclusion 205References 20510 Deep Learning-Based Intrusion Detection in Wireless Networks 209Rahila Rahim and Mohammad Ahsan Chishti10.1 Introduction 20910.1.1 Mobile Ad Hoc Networks 21010.1.1.1 Components 21010.1.2 Wireless Sensor Networks (WSNs) 21210.2 The Importance of Security in Wireless Networks 21410.3 Challenges of Intrusion Detection Systems (IDS) in MANETs and WSNs 21510.3.1 Dynamic Topology and Resource Constraints 21510.3.2 Advanced Threats 21710.3.3 Flexibility and Distributed Processing 21710.3.4 Privacy Concerns 21710.3.5 Wireless Medium Weaknesses 21810.4 Intrusion Detection Systems 21810.5 Applications of Deep Learning in Bolstering Security Across Wireless Networks 22010.5.1 Neural Networks 22110.5.2 Artificial Neural Networks (ANNs) 22210.5.3 Conventional Neural Networks (CNNs) 22310.5.4 Recurrent Neural Networks (RNNs) 22310.5.5 Long Short-Term Memory 22510.6 Deep Learning-Based Solutions for the Challenges of Intrusion Detection in MANETs and WSNs 22810.6.1 Use Case for Autoencoders in Resource-Constrained Wireless Networks 22810.6.2 Use Cases of CNNs for DDoS Detection 229References 22911 Deep Learning-Based Intrusion Detection in Wireless Networks 233Shadab Alam and Sadaf Ahmad11.1 Introduction 23311.2 Wireless Network Security and IDS Challenges 23411.3 Deep Learning for Intrusion Detection 23511.3.1 Role of Deep Learning in IDS 23511.3.2 Enhancing IDS with Emerging Technologies 23511.4 Common Deep Learning Architectures for IDS 23611.4.1 Convolutional Neural Networks (CNNs) for IDSs 23611.4.2 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (lstm) 23611.4.3 Autoencoders for Anomaly Detection 23711.4.4 Generative Adversarial Networks (GANs) for IDSs 23711.4.5 Transformer-Based Models for Scalable IDS 23711.4.6 Comparative Analysis of Deep Learning Architectures 23711.5 Applications of Deep Learning-Based IDS 23811.5.1 IDSs in IoT Security 23811.5.1.1 Security Challenges in Resource-Constrained IoT Devices 23911.5.1.2 Deploying Lightweight Deep Learning Models on IoT Gateways 23911.5.1.3 Real-World Implementations 23911.5.2 IDSs in 5G and Beyond Networks 24011.5.2.1 Emerging Threats in 5G and Future Wireless Networks 24011.5.2.2 AI-Driven Network Slicing for Intrusion Detection 24011.5.2.3 Real-World Implementations 24111.5.3 IDS for Mobile Ad Hoc Networks (MANETs) 24111.5.3.1 Challenges of Decentralized and Dynamic MANET Security 24111.5.3.2 Deep Learning Techniques for Detecting Routing Attacks 24111.5.3.3 Real-World Implementations 24211.6 Challenges and Future Research Directions 24211.6.1 Adversarial Attacks Against Deep Learning IDS 24211.6.1.1 Techniques Used by Attackers to Evade IDSs 24211.6.1.2 Defensive Measures: Adversarial Training and Robust IDS Models 24311.6.2 Computational Overhead and Energy Efficiency 24311.6.2.1 Issues in Deploying Deep Learning Models on Resource-Constrained Devices 24311.6.2.2 Optimization Techniques: Model Pruning, Quantization, and Knowledge Distillation 24311.6.3 Real-Time Deployment Challenges 24411.6.4 Issues with Processing High-Speed Network Traffic 24411.6.4.1 Combining Deep Learning with Rule-Based and Hybrid IDSs 24411.6.5 Explainability and Interpretability of IDS Models 24411.6.5.1 Need for Trust and Transparency in AI-Driven Security 24511.6.5.2 Explainable AI (XAI) Approaches 24511.6.5.3 Benefits of XAI in IDSs 24511.6.6 Integration with Edge Computing and Federated Learning 24511.6.6.1 Distributed IDSs for Large-Scale, Decentralized Networks 24611.6.6.2 Privacy-Preserving AI for Intrusion Detection 24611.6.7 Future Directions in IDS Research 24611.6.7.1 Quantum AI for Cybersecurity 24611.6.7.2 AI-Driven Autonomous IDSs with Reinforcement Learning 24611.6.7.3 Blockchain-Based Intrusion Detection 24611.7 Conclusion 247References 24712 Securing IoT Environments: Deep Learning-Based Intrusion Detection 251Ashish K. Sharma, Neha Purohit, Shubhalaxmi Joshi, Itika Umesh Lakkewar, and Prashant Khobragade12.1 Introduction 25112.2 Overview of IoT Security Challenges 25312.3 Deep Learning for Intrusion Detection in IoT 25512.3.1 Implementation Examples 25612.4 Ensuring Data Safety and Privacy in Deep Learning-Based Intrusion Detection Systems (IDSs) 25612.4.1 Data Security Measures 25612.4.1.1 Encryption Techniques for IoT Data 25612.4.1.2 Role of Secure Communication Protocols 25712.4.2 Privacy Protection 25712.4.2.1 Differential Privacy Techniques in Data Sharing 25712.4.2.2 Edge Computing to Reduce Exposure of Sensitive Data 25812.4.3 Case Studies 25812.5 IoT Operations with Security Optimization 25912.5.1 Role of Intrusion Detection in Efficiency 25912.5.2 Bridge Between Security and Performance 25912.6 Challenges and Future Directions 26012.6.1 Current Limitations 26012.6.2 Research Opportunities 26012.6.3 Future Vision 26112.7 Conclusion 261References 26213 A Deep Learning Approach for the Detection of Zero-day Attacks 267Aamir S. Ahanger, Asra Khanam, Faheem Syeed Masoodi, and Bilal Ahmad Pandow13.1 Introduction 26713.2 Network Vulnerabilities 27113.3 Anomalies and Anomalies in Networks 27113.4 Deep Learning 27213.5 Vulnerabilities in Hardware and Software 27313.6 Network Configuration Vulnerabilities 27413.7 Network Hardware Vulnerabilities 27413.8 Network Perimeter Vulnerabilities 27513.9 Network Monitoring and Logging Vulnerabilities 27513.10 Communication Vulnerabilities 27513.11 Wireless Connection Vulnerabilities 27613.12 Cyberattacks That Exploit Vulnerabilities 27613.13 Denial of Service (DoS) 27713.14 User to Root (U2R) Attacks 27713.15 Remote to Local (R2L) Attacks 27813.16 Probe Attacks 27813.17 Deep Learning to Detect and Mitigate Zero-day Attacks 278References 280Index 285

FAHEEM SYEED MASOODI, PHD, is an Associate Professor of Cybersecurity at Bahrain Polytechnic University. He previously served at the University of Kashmir and the Jazan University in Saudi Arabia. He holds a PhD in Network Security and Cryptography and has published extensively in cryptography, intrusion detection, post-quantum cryptography, financial security, and IoT. His contributions include several books, high-impact papers, and fellowships from France, Brazil, India, and Malaysia.ALWI BAMHDI, PHD, is an Associate Professor in the Computer Sciences Department at Umm ul Qura University, Saudi Arabia. His research interests include mobile ad hoc networks, wireless sensor networks, and information security.

• About the Editors xixList of Contributors xxiForeword xxvPreface xxviiAcknowledgments xxix1 Intrusion Detection in the Age of Deep Learning: An Introduction 1Faheem Syeed Masoodi1.1 Introduction 11.1.1 The Pioneers of Network Security 21.1.1.1 Limitations of the Existing System 21.1.2 How Firewalls Are Different from IDS 31.1.3 Need for Intrusion Detection Systems 41.1.4 Intrusion Detection System 51.1.4.1 Intrusion Detection Technologies 91.1.4.2 Intrusion Detection Methodologies 141.1.4.3 Intrusion Detection Approaches 171.1.5 Need for Deep Learning Based IDS 21References 222 Machine Learning for Intrusion Detection 25Divya M.K.2.1 Introduction 252.1.1 Overview of Intrusion Detection Systems (IDSs) 252.1.1.1 Types of IDSs: Host-Based, Network-Based, Hybrid 262.2 Role of Machine Learning in IDSs 292.2.1 Benefits and Challenges of Using Machine Learning in IDSs 292.2.1.1 Benefits of ML in IDSs 292.2.1.2 Challenges of ML in IDS 292.2.2 Evolution from Traditional Methods to ML-Based Approaches in IDSs 302.2.2.1 Traditional Methods in IDSs 302.2.2.2 Transition to ML-Based Approaches 312.2.2.3 Current ML-Based IDS Landscape 312.3 Fundamentals of Machine Learning 322.3.1 Key ML Techniques 322.3.1.1 How These Concepts Enable Pattern and Anomaly Detection 332.3.2 Key Algorithms Used in Intrusion Detection 332.3.3 Classification Algorithms 332.3.3.1 Clustering Algorithms 342.3.3.2 Anomaly Detection Algorithms 352.4 Data Preparation for IDSs 352.4.1 Types of Data Used in IDSs 362.4.2 Data Preprocessing Techniques 372.5 Supervised Learning for Intrusion Detection 372.5.1 Key Components of Supervised Learning 372.5.2 Benefits of Supervised Learning in IDSs 382.5.3 Challenges of Supervised Learning in IDSs 382.5.4 Common Supervised Learning Techniques in IDSs 392.5.5 Supervised Learning Algorithms 392.5.6 Practical Example: Using Supervised Learning in IDSs 412.6 Unsupervised Learning for Intrusion Detection Systems (IDSs) 412.6.1 Techniques and Algorithms 432.6.2 Example Use Case: Anomaly-Based Network Intrusion Detection 442.7 Semi-Supervised Learning in Intrusion Detection Systems (IDSs) 442.7.1 Semi-Supervised Algorithms and Applications 462.7.2 Applications in IDSs 482.7.3 Example Use Case: Semi-Supervised Network Intrusion Detection 492.8 Reinforcement Learning for Intrusion Detection System 492.8.1 Example Scenario 512.9 Feature Engineering, Model Training, and Hyperparameter Tuning in Ids 532.9.1 Feature Engineering in IDS 532.9.2 Model Training in IDS 542.9.3 Hyperparameter Tuning in IDSs 552.9.4 Practical Implementation Challenges in IDSs 56References 563 Deep Learning Fundamentals-I 59Razeef Mohd and Abeena Mohiudin Azad3.1 Introduction to Deep Learning 593.1.1 Definition and Importance 593.1.2 Deep Learning in Cybersecurity: Enhancing Threat Detection and Prevention 613.1.3 Key Areas Where Deep Learning Enhances Cybersecurity 613.1.3.1 Proactive Threat Detection with Deep Learning 623.2 Conceptual Foundations of Deep Learning 633.2.1 Historical Evolution of Deep Learning 633.2.2 Key Differences Between Deep Learning and Traditional Machine Learning 643.2.3 Why Deep Learning Is Suited for Intrusion Detection 643.2.4 Artificial Neural Networks (ANNs) as the Core of Deep Learning 653.2.4.1 Structure of ANNs 653.2.4.2 Working Mechanism of ANNs 653.2.4.3 The Role of Deep Learning in Pattern Recognition and Anomaly Detection 663.3 Neural Networks: The Building Blocks of Deep Learning 663.3.1 Biological Inspiration and Mathematical Representation 663.3.2 Architecture of Neural Networks (Layers, Activation Functions, and Weights) 673.3.2.1 Layers in Neural Networks 673.3.2.2 Neuron Activation Function 683.3.2.3 Types of Activation Functions 683.3.3 Training Deep Learning Models Using Backpropagation and Weight Optimization 693.3.3.1 Error Functions in Neural Networks 703.3.3.2 Steps in Backpropagation 703.3.4 Gradient Descent: The Backbone of Learning in Neural Networks 713.3.4.1 Advanced Optimization Techniques 723.3.5 Regularization Techniques in Neural Networks 733.3.5.1 L1 and L2 Regularization 733.3.6 Dropout: Reducing Overfitting 733.3.6.1 Impact of Activation Functions and Optimization on Deep Learning 743.4 Applications of Deep Learning in Intrusion Detection 753.4.1 Types of Cyber Threats and Attacks 753.4.1.1 DDoS Attacks 753.4.1.2 Malware and Ransomware 753.4.1.3 Brute Force Attacks 753.4.1.4 Insider Threats 763.4.2 Deep Learning-Based Intrusion Detection Systems (IDSs) 763.4.2.1 Signature-Based IDS 763.4.2.2 Anomaly-Based IDS 763.4.2.3 Deep Learning Models Commonly Used for IDSs 773.4.3 Case Studies and Real-World Implementations 773.4.3.1 Financial Institutions 773.4.3.2 Technology Companies 783.4.3.3 Healthcare Organizations 783.4.3.4 Government Agencies 783.4.3.5 Retail and E-Commerce 783.5 Security-Enhancing Potential of Deep Learning 793.5.1 Advantages of Deep Learning in Cybersecurity 793.5.1.1 Automated Threat Detection 793.5.1.2 High Accuracy 793.5.1.3 Scalability 803.5.1.4 Adaptability to Evolving Threats 803.5.1.5 Reduced False Positives 803.5.2 Challenges and Limitations of Deep Learning-Based IDS 803.5.2.1 Computational Costs 813.5.2.2 Adversarial Attacks 813.5.2.3 Data Availability and Quality 813.5.3 Future Directions in AI-Driven Intrusion Detection 823.5.3.1 Federated Learning 823.5.3.2 Explainable AI (XAI) 823.5.3.3 Integration with Blockchain 823.5.3.4 Continuous Learning and Adaptation 833.6 Conclusion 833.6.1 Summary of Key Insights 833.6.2 Future Directions in Deep Learning for Cybersecurity 84References 844 Deep Learning Fundamentals-II 91Saduf Afzal, Shifaa Basharat, and Shozab Khurshid4.1 Introduction 914.2 Artificial Neural Networks 924.3 Overview of Deep Learning 944.4 Deep Learning Algorithms 954.4.1 Deep Neural Networks (DNNs) 954.4.2 Deep Belief Networks 964.4.3 Autoencoders 974.4.4 Convolutional Neural Network 984.4.5 Recurrent Neural Networks 994.5 Conclusion 102References 1025 Intrusion Detection Through Deep Learning: Emerging Trends and Challenges 107Achyutananda Mishra5.1 Introduction 1075.2 Deep Learning 1085.2.1 Neural Network Architectures 1095.2.2 Types of Neural Networks 1105.2.2.1 Feed-forward Neural Networks (FNNs) 1105.2.2.2 Convolutional Neural Networks (CNNs) 1115.2.2.3 Recurrent Neural Networks (RNNs) 1115.2.2.4 Recursive Neural Networks (RvNNs) 1125.3 Applications of Deep Learning 1125.4 Intrusion Detection 1135.4.1 Classification 1165.5 Methodologies of Detection 1165.6 Deep Learning for Intrusion Detection 1175.7 Limitations 1195.7.1 Mr. William’s Case 1195.7.2 Challenges 1205.8 Conclusion 120References 1216 Dataset for Evaluating Deep Learning-Based Intrusion Detection 125Wasia Ashraf, Faheem Syeed Masoodi, and Asra Khanam6.1 Introduction 1256.2 Data 1266.2.1 Packet-Based Data 1266.2.2 Flow-Based Data 1276.2.3 Other Data 1276.3 Dataset Properties 1286.3.1 Basic Information 1286.3.2 Nature of Data 1296.3.3 Data Volume 1296.3.4 Recording Environment 1296.3.5 Evaluation 1306.4 Datasets 1316.4.1 Darpa 1316.4.2 Kdd 1999 1336.4.3 Nsl-kdd 1346.4.4 Iscx- 2012 1376.4.5 Unsw-nb 15 1396.4.6 Cic-ids- 2017 1416.5 Conclusion 143References 1447 Deep Learning Features: Techniques for Extraction and Selection 147K.S. Shashikala, Sneha Shinde, Sandyarani Vadlamudi, and Mahendra Shridhar Naik7.1 Introduction 1477.1.1 Overview of Intrusion Detection Systems (IDSs) 1477.1.2 Role of Deep Learning in IDSs 1487.1.3 Importance of Feature Extraction and Selection 1497.1.3.1 Feature Extraction 1497.1.3.2 Feature Selection 1497.1.3.3 Critical Role in IDSs 1507.1.4 Improvement in Accuracy, Complexity Reduction, and Efficiency Enhancement 1507.1.5 Challenges in Managing High-Dimensional Data in IDSs 1527.2 Techniques for Feature Extraction and Selection 1537.2.1 Principal Component Analysis 1537.2.2 Linear Discriminant Analysis 1537.2.3 Mutual Information 1547.2.3.1 How Mutual Information Works? 1547.2.4 Chi-Squared Feature Selection 1557.2.4.1 How Chi-Squared Feature Selection Works? 1557.2.5 Comparative Analysis of Techniques 1567.3 Applications in Intrusion Detection Systems 1587.3.1 Integrating Feature Extraction and Selection in IDS Workflows 1587.3.1.1 Impact on Performance 1597.3.1.2 Challenges in Real-World Applications 1597.3.2 Performance Improvements 1597.3.2.1 Efficiency Gains Through MI and Chi-Squared Methods 1597.3.2.2 Enhancing Scalability for Growing Network Demands 1607.3.3 Practical Deployment 1607.3.3.1 Preprocessing with PCA and LDA 1607.3.3.2 Training with MI and Chi-Squared Methods 1617.3.3.3 Hybrid Approaches for Enhanced Results 1617.3.3.4 Real-World Applications 1617.4 Conclusion and Future Trends 1627.4.1 Key Insights 1627.4.2 Future Directions 163References 1648 Exploring Advanced Artificial Intelligence for Anomaly Detection 167Palanisamy Padmaloshani8.1 Introduction 1678.1.1 Types of Anomalous Detection 1678.1.2 Artificial Intelligence-Based Anomaly Detection 1688.1.2.1 AI-Based AD Process 1688.1.2.2 Machine Learning Algorithms for AD 1688.1.2.3 Application Domains 1698.1.2.4 Advantages of AI-Based AD Methods 1708.1.2.5 Challenges in AI-Based AD 1708.1.2.6 AI-Based AD Methods 1708.2 Autoencoder-Based Anomaly Detection 1718.2.1 Types of Autoencoders 1728.3 Generative Adversarial Networks Anomaly Detection 1738.3.1 Features of GANs 1738.3.2 Working Principle of GANs 1748.4 One-Class Classification Anomaly Detection 1758.5 Deep Reinforcement Learning Anomaly Detection 1778.6 Recurrent Neural Networks-Based Anomaly Detection 1788.7 Transfer Learning Anomaly Detection 1798.8 Conclusion 181References 1819 Enhancing Security in Smart Environments Using Deep Learning: A Comprehensive Approach 185Syed Irfan Yaqoob, Preet Kamal, Shivani Aggarwal, Anuradha Kanade, and Shantanu Kanade9.1 Introduction 1859.1.1 Understanding Smart Environments and Their Security Needs 1879.1.2 Connectivity: The Backbone of Smart Environments 1879.2 Automation: Autonomous Decision-Making for Efficiency 1889.3 Data Collection and Analytics: Leveraging Big Data for Optimization 1899.4 Data Privacy and Integrity 1909.5 Authentication and Access Control 1919.6 Intrusion Detection 1929.7 Adaptability to Evolving Threats 1939.8 The Role of Deep Learning in Security Enhancement 1949.8.1 Anomaly Detection with Deep Learning 1949.8.1.1 Unsupervised Learning: Detecting Novel Security Threats 1949.8.1.2 High-dimensional Data Processing: Learning Complex Patterns in Smart Environments 1959.8.2 Real-Time Analysis: Continuous Monitoring and Threat Detection 1969.8.3 Intrusion Detection Systems (IDSs) Using Deep Learning 1979.8.4 Adaptive Defense Mechanisms 1989.8.4.1 Self-learning Systems: Evolving with New Data 1989.8.4.2 Threat Prediction and Prevention: Anticipating Attacks Before They Happen 1999.8.4.3 Automated Response: Mitigating Threats Instantly 2009.8.4.4 Interdisciplinary Collaboration for Enhanced Security 2019.8.5 Cybersecurity and Machine Learning Experts: Designing Security Solutions for Smart Environments 2019.8.6 Device Manufacturers and IoT Developers: Building Security into Hardware and Software 2029.8.7 Ethics and Privacy Experts: Ensuring User Privacy and Trust 2039.8.8 Policymakers and Regulators: Establishing Standards and Regulations 2049.9 Challenges and Future Directions 2049.10 Conclusion 205References 20510 Deep Learning-Based Intrusion Detection in Wireless Networks 209Rahila Rahim and Mohammad Ahsan Chishti10.1 Introduction 20910.1.1 Mobile Ad Hoc Networks 21010.1.1.1 Components 21010.1.2 Wireless Sensor Networks (WSNs) 21210.2 The Importance of Security in Wireless Networks 21410.3 Challenges of Intrusion Detection Systems (IDS) in MANETs and WSNs 21510.3.1 Dynamic Topology and Resource Constraints 21510.3.2 Advanced Threats 21710.3.3 Flexibility and Distributed Processing 21710.3.4 Privacy Concerns 21710.3.5 Wireless Medium Weaknesses 21810.4 Intrusion Detection Systems 21810.5 Applications of Deep Learning in Bolstering Security Across Wireless Networks 22010.5.1 Neural Networks 22110.5.2 Artificial Neural Networks (ANNs) 22210.5.3 Conventional Neural Networks (CNNs) 22310.5.4 Recurrent Neural Networks (RNNs) 22310.5.5 Long Short-Term Memory 22510.6 Deep Learning-Based Solutions for the Challenges of Intrusion Detection in MANETs and WSNs 22810.6.1 Use Case for Autoencoders in Resource-Constrained Wireless Networks 22810.6.2 Use Cases of CNNs for DDoS Detection 229References 22911 Deep Learning-Based Intrusion Detection in Wireless Networks 233Shadab Alam and Sadaf Ahmad11.1 Introduction 23311.2 Wireless Network Security and IDS Challenges 23411.3 Deep Learning for Intrusion Detection 23511.3.1 Role of Deep Learning in IDS 23511.3.2 Enhancing IDS with Emerging Technologies 23511.4 Common Deep Learning Architectures for IDS 23611.4.1 Convolutional Neural Networks (CNNs) for IDSs 23611.4.2 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (lstm) 23611.4.3 Autoencoders for Anomaly Detection 23711.4.4 Generative Adversarial Networks (GANs) for IDSs 23711.4.5 Transformer-Based Models for Scalable IDS 23711.4.6 Comparative Analysis of Deep Learning Architectures 23711.5 Applications of Deep Learning-Based IDS 23811.5.1 IDSs in IoT Security 23811.5.1.1 Security Challenges in Resource-Constrained IoT Devices 23911.5.1.2 Deploying Lightweight Deep Learning Models on IoT Gateways 23911.5.1.3 Real-World Implementations 23911.5.2 IDSs in 5G and Beyond Networks 24011.5.2.1 Emerging Threats in 5G and Future Wireless Networks 24011.5.2.2 AI-Driven Network Slicing for Intrusion Detection 24011.5.2.3 Real-World Implementations 24111.5.3 IDS for Mobile Ad Hoc Networks (MANETs) 24111.5.3.1 Challenges of Decentralized and Dynamic MANET Security 24111.5.3.2 Deep Learning Techniques for Detecting Routing Attacks 24111.5.3.3 Real-World Implementations 24211.6 Challenges and Future Research Directions 24211.6.1 Adversarial Attacks Against Deep Learning IDS 24211.6.1.1 Techniques Used by Attackers to Evade IDSs 24211.6.1.2 Defensive Measures: Adversarial Training and Robust IDS Models 24311.6.2 Computational Overhead and Energy Efficiency 24311.6.2.1 Issues in Deploying Deep Learning Models on Resource-Constrained Devices 24311.6.2.2 Optimization Techniques: Model Pruning, Quantization, and Knowledge Distillation 24311.6.3 Real-Time Deployment Challenges 24411.6.4 Issues with Processing High-Speed Network Traffic 24411.6.4.1 Combining Deep Learning with Rule-Based and Hybrid IDSs 24411.6.5 Explainability and Interpretability of IDS Models 24411.6.5.1 Need for Trust and Transparency in AI-Driven Security 24511.6.5.2 Explainable AI (XAI) Approaches 24511.6.5.3 Benefits of XAI in IDSs 24511.6.6 Integration with Edge Computing and Federated Learning 24511.6.6.1 Distributed IDSs for Large-Scale, Decentralized Networks 24611.6.6.2 Privacy-Preserving AI for Intrusion Detection 24611.6.7 Future Directions in IDS Research 24611.6.7.1 Quantum AI for Cybersecurity 24611.6.7.2 AI-Driven Autonomous IDSs with Reinforcement Learning 24611.6.7.3 Blockchain-Based Intrusion Detection 24611.7 Conclusion 247References 24712 Securing IoT Environments: Deep Learning-Based Intrusion Detection 251Ashish K. Sharma, Neha Purohit, Shubhalaxmi Joshi, Itika Umesh Lakkewar, and Prashant Khobragade12.1 Introduction 25112.2 Overview of IoT Security Challenges 25312.3 Deep Learning for Intrusion Detection in IoT 25512.3.1 Implementation Examples 25612.4 Ensuring Data Safety and Privacy in Deep Learning-Based Intrusion Detection Systems (IDSs) 25612.4.1 Data Security Measures 25612.4.1.1 Encryption Techniques for IoT Data 25612.4.1.2 Role of Secure Communication Protocols 25712.4.2 Privacy Protection 25712.4.2.1 Differential Privacy Techniques in Data Sharing 25712.4.2.2 Edge Computing to Reduce Exposure of Sensitive Data 25812.4.3 Case Studies 25812.5 IoT Operations with Security Optimization 25912.5.1 Role of Intrusion Detection in Efficiency 25912.5.2 Bridge Between Security and Performance 25912.6 Challenges and Future Directions 26012.6.1 Current Limitations 26012.6.2 Research Opportunities 26012.6.3 Future Vision 26112.7 Conclusion 261References 26213 A Deep Learning Approach for the Detection of Zero-day Attacks 267Aamir S. Ahanger, Asra Khanam, Faheem Syeed Masoodi, and Bilal Ahmad Pandow13.1 Introduction 26713.2 Network Vulnerabilities 27113.3 Anomalies and Anomalies in Networks 27113.4 Deep Learning 27213.5 Vulnerabilities in Hardware and Software 27313.6 Network Configuration Vulnerabilities 27413.7 Network Hardware Vulnerabilities 27413.8 Network Perimeter Vulnerabilities 27513.9 Network Monitoring and Logging Vulnerabilities 27513.10 Communication Vulnerabilities 27513.11 Wireless Connection Vulnerabilities 27613.12 Cyberattacks That Exploit Vulnerabilities 27613.13 Denial of Service (DoS) 27713.14 User to Root (U2R) Attacks 27713.15 Remote to Local (R2L) Attacks 27813.16 Probe Attacks 27813.17 Deep Learning to Detect and Mitigate Zero-day Attacks 278References 280Index 285

FAHEEM SYEED MASOODI, PHD, is an Associate Professor of Cybersecurity at Bahrain Polytechnic University. He previously served at the University of Kashmir and the Jazan University in Saudi Arabia. He holds a PhD in Network Security and Cryptography and has published extensively in cryptography, intrusion detection, post-quantum cryptography, financial security, and IoT. His contributions include several books, high-impact papers, and fellowships from France, Brazil, India, and Malaysia.ALWI BAMHDI, PHD, is an Associate Professor in the Computer Sciences Department at Umm ul Qura University, Saudi Arabia. His research interests include mobile ad hoc networks, wireless sensor networks, and information security.

• About the Editors xixList of Contributors xxiForeword xxvPreface xxviiAcknowledgments xxix1 Intrusion Detection in the Age of Deep Learning: An Introduction 1Faheem Syeed Masoodi1.1 Introduction 11.1.1 The Pioneers of Network Security 21.1.1.1 Limitations of the Existing System 21.1.2 How Firewalls Are Different from IDS 31.1.3 Need for Intrusion Detection Systems 41.1.4 Intrusion Detection System 51.1.4.1 Intrusion Detection Technologies 91.1.4.2 Intrusion Detection Methodologies 141.1.4.3 Intrusion Detection Approaches 171.1.5 Need for Deep Learning Based IDS 21References 222 Machine Learning for Intrusion Detection 25Divya M.K.2.1 Introduction 252.1.1 Overview of Intrusion Detection Systems (IDSs) 252.1.1.1 Types of IDSs: Host-Based, Network-Based, Hybrid 262.2 Role of Machine Learning in IDSs 292.2.1 Benefits and Challenges of Using Machine Learning in IDSs 292.2.1.1 Benefits of ML in IDSs 292.2.1.2 Challenges of ML in IDS 292.2.2 Evolution from Traditional Methods to ML-Based Approaches in IDSs 302.2.2.1 Traditional Methods in IDSs 302.2.2.2 Transition to ML-Based Approaches 312.2.2.3 Current ML-Based IDS Landscape 312.3 Fundamentals of Machine Learning 322.3.1 Key ML Techniques 322.3.1.1 How These Concepts Enable Pattern and Anomaly Detection 332.3.2 Key Algorithms Used in Intrusion Detection 332.3.3 Classification Algorithms 332.3.3.1 Clustering Algorithms 342.3.3.2 Anomaly Detection Algorithms 352.4 Data Preparation for IDSs 352.4.1 Types of Data Used in IDSs 362.4.2 Data Preprocessing Techniques 372.5 Supervised Learning for Intrusion Detection 372.5.1 Key Components of Supervised Learning 372.5.2 Benefits of Supervised Learning in IDSs 382.5.3 Challenges of Supervised Learning in IDSs 382.5.4 Common Supervised Learning Techniques in IDSs 392.5.5 Supervised Learning Algorithms 392.5.6 Practical Example: Using Supervised Learning in IDSs 412.6 Unsupervised Learning for Intrusion Detection Systems (IDSs) 412.6.1 Techniques and Algorithms 432.6.2 Example Use Case: Anomaly-Based Network Intrusion Detection 442.7 Semi-Supervised Learning in Intrusion Detection Systems (IDSs) 442.7.1 Semi-Supervised Algorithms and Applications 462.7.2 Applications in IDSs 482.7.3 Example Use Case: Semi-Supervised Network Intrusion Detection 492.8 Reinforcement Learning for Intrusion Detection System 492.8.1 Example Scenario 512.9 Feature Engineering, Model Training, and Hyperparameter Tuning in Ids 532.9.1 Feature Engineering in IDS 532.9.2 Model Training in IDS 542.9.3 Hyperparameter Tuning in IDSs 552.9.4 Practical Implementation Challenges in IDSs 56References 563 Deep Learning Fundamentals-I 59Razeef Mohd and Abeena Mohiudin Azad3.1 Introduction to Deep Learning 593.1.1 Definition and Importance 593.1.2 Deep Learning in Cybersecurity: Enhancing Threat Detection and Prevention 613.1.3 Key Areas Where Deep Learning Enhances Cybersecurity 613.1.3.1 Proactive Threat Detection with Deep Learning 623.2 Conceptual Foundations of Deep Learning 633.2.1 Historical Evolution of Deep Learning 633.2.2 Key Differences Between Deep Learning and Traditional Machine Learning 643.2.3 Why Deep Learning Is Suited for Intrusion Detection 643.2.4 Artificial Neural Networks (ANNs) as the Core of Deep Learning 653.2.4.1 Structure of ANNs 653.2.4.2 Working Mechanism of ANNs 653.2.4.3 The Role of Deep Learning in Pattern Recognition and Anomaly Detection 663.3 Neural Networks: The Building Blocks of Deep Learning 663.3.1 Biological Inspiration and Mathematical Representation 663.3.2 Architecture of Neural Networks (Layers, Activation Functions, and Weights) 673.3.2.1 Layers in Neural Networks 673.3.2.2 Neuron Activation Function 683.3.2.3 Types of Activation Functions 683.3.3 Training Deep Learning Models Using Backpropagation and Weight Optimization 693.3.3.1 Error Functions in Neural Networks 703.3.3.2 Steps in Backpropagation 703.3.4 Gradient Descent: The Backbone of Learning in Neural Networks 713.3.4.1 Advanced Optimization Techniques 723.3.5 Regularization Techniques in Neural Networks 733.3.5.1 L1 and L2 Regularization 733.3.6 Dropout: Reducing Overfitting 733.3.6.1 Impact of Activation Functions and Optimization on Deep Learning 743.4 Applications of Deep Learning in Intrusion Detection 753.4.1 Types of Cyber Threats and Attacks 753.4.1.1 DDoS Attacks 753.4.1.2 Malware and Ransomware 753.4.1.3 Brute Force Attacks 753.4.1.4 Insider Threats 763.4.2 Deep Learning-Based Intrusion Detection Systems (IDSs) 763.4.2.1 Signature-Based IDS 763.4.2.2 Anomaly-Based IDS 763.4.2.3 Deep Learning Models Commonly Used for IDSs 773.4.3 Case Studies and Real-World Implementations 773.4.3.1 Financial Institutions 773.4.3.2 Technology Companies 783.4.3.3 Healthcare Organizations 783.4.3.4 Government Agencies 783.4.3.5 Retail and E-Commerce 783.5 Security-Enhancing Potential of Deep Learning 793.5.1 Advantages of Deep Learning in Cybersecurity 793.5.1.1 Automated Threat Detection 793.5.1.2 High Accuracy 793.5.1.3 Scalability 803.5.1.4 Adaptability to Evolving Threats 803.5.1.5 Reduced False Positives 803.5.2 Challenges and Limitations of Deep Learning-Based IDS 803.5.2.1 Computational Costs 813.5.2.2 Adversarial Attacks 813.5.2.3 Data Availability and Quality 813.5.3 Future Directions in AI-Driven Intrusion Detection 823.5.3.1 Federated Learning 823.5.3.2 Explainable AI (XAI) 823.5.3.3 Integration with Blockchain 823.5.3.4 Continuous Learning and Adaptation 833.6 Conclusion 833.6.1 Summary of Key Insights 833.6.2 Future Directions in Deep Learning for Cybersecurity 84References 844 Deep Learning Fundamentals-II 91Saduf Afzal, Shifaa Basharat, and Shozab Khurshid4.1 Introduction 914.2 Artificial Neural Networks 924.3 Overview of Deep Learning 944.4 Deep Learning Algorithms 954.4.1 Deep Neural Networks (DNNs) 954.4.2 Deep Belief Networks 964.4.3 Autoencoders 974.4.4 Convolutional Neural Network 984.4.5 Recurrent Neural Networks 994.5 Conclusion 102References 1025 Intrusion Detection Through Deep Learning: Emerging Trends and Challenges 107Achyutananda Mishra5.1 Introduction 1075.2 Deep Learning 1085.2.1 Neural Network Architectures 1095.2.2 Types of Neural Networks 1105.2.2.1 Feed-forward Neural Networks (FNNs) 1105.2.2.2 Convolutional Neural Networks (CNNs) 1115.2.2.3 Recurrent Neural Networks (RNNs) 1115.2.2.4 Recursive Neural Networks (RvNNs) 1125.3 Applications of Deep Learning 1125.4 Intrusion Detection 1135.4.1 Classification 1165.5 Methodologies of Detection 1165.6 Deep Learning for Intrusion Detection 1175.7 Limitations 1195.7.1 Mr. William’s Case 1195.7.2 Challenges 1205.8 Conclusion 120References 1216 Dataset for Evaluating Deep Learning-Based Intrusion Detection 125Wasia Ashraf, Faheem Syeed Masoodi, and Asra Khanam6.1 Introduction 1256.2 Data 1266.2.1 Packet-Based Data 1266.2.2 Flow-Based Data 1276.2.3 Other Data 1276.3 Dataset Properties 1286.3.1 Basic Information 1286.3.2 Nature of Data 1296.3.3 Data Volume 1296.3.4 Recording Environment 1296.3.5 Evaluation 1306.4 Datasets 1316.4.1 Darpa 1316.4.2 Kdd 1999 1336.4.3 Nsl-kdd 1346.4.4 Iscx- 2012 1376.4.5 Unsw-nb 15 1396.4.6 Cic-ids- 2017 1416.5 Conclusion 143References 1447 Deep Learning Features: Techniques for Extraction and Selection 147K.S. Shashikala, Sneha Shinde, Sandyarani Vadlamudi, and Mahendra Shridhar Naik7.1 Introduction 1477.1.1 Overview of Intrusion Detection Systems (IDSs) 1477.1.2 Role of Deep Learning in IDSs 1487.1.3 Importance of Feature Extraction and Selection 1497.1.3.1 Feature Extraction 1497.1.3.2 Feature Selection 1497.1.3.3 Critical Role in IDSs 1507.1.4 Improvement in Accuracy, Complexity Reduction, and Efficiency Enhancement 1507.1.5 Challenges in Managing High-Dimensional Data in IDSs 1527.2 Techniques for Feature Extraction and Selection 1537.2.1 Principal Component Analysis 1537.2.2 Linear Discriminant Analysis 1537.2.3 Mutual Information 1547.2.3.1 How Mutual Information Works? 1547.2.4 Chi-Squared Feature Selection 1557.2.4.1 How Chi-Squared Feature Selection Works? 1557.2.5 Comparative Analysis of Techniques 1567.3 Applications in Intrusion Detection Systems 1587.3.1 Integrating Feature Extraction and Selection in IDS Workflows 1587.3.1.1 Impact on Performance 1597.3.1.2 Challenges in Real-World Applications 1597.3.2 Performance Improvements 1597.3.2.1 Efficiency Gains Through MI and Chi-Squared Methods 1597.3.2.2 Enhancing Scalability for Growing Network Demands 1607.3.3 Practical Deployment 1607.3.3.1 Preprocessing with PCA and LDA 1607.3.3.2 Training with MI and Chi-Squared Methods 1617.3.3.3 Hybrid Approaches for Enhanced Results 1617.3.3.4 Real-World Applications 1617.4 Conclusion and Future Trends 1627.4.1 Key Insights 1627.4.2 Future Directions 163References 1648 Exploring Advanced Artificial Intelligence for Anomaly Detection 167Palanisamy Padmaloshani8.1 Introduction 1678.1.1 Types of Anomalous Detection 1678.1.2 Artificial Intelligence-Based Anomaly Detection 1688.1.2.1 AI-Based AD Process 1688.1.2.2 Machine Learning Algorithms for AD 1688.1.2.3 Application Domains 1698.1.2.4 Advantages of AI-Based AD Methods 1708.1.2.5 Challenges in AI-Based AD 1708.1.2.6 AI-Based AD Methods 1708.2 Autoencoder-Based Anomaly Detection 1718.2.1 Types of Autoencoders 1728.3 Generative Adversarial Networks Anomaly Detection 1738.3.1 Features of GANs 1738.3.2 Working Principle of GANs 1748.4 One-Class Classification Anomaly Detection 1758.5 Deep Reinforcement Learning Anomaly Detection 1778.6 Recurrent Neural Networks-Based Anomaly Detection 1788.7 Transfer Learning Anomaly Detection 1798.8 Conclusion 181References 1819 Enhancing Security in Smart Environments Using Deep Learning: A Comprehensive Approach 185Syed Irfan Yaqoob, Preet Kamal, Shivani Aggarwal, Anuradha Kanade, and Shantanu Kanade9.1 Introduction 1859.1.1 Understanding Smart Environments and Their Security Needs 1879.1.2 Connectivity: The Backbone of Smart Environments 1879.2 Automation: Autonomous Decision-Making for Efficiency 1889.3 Data Collection and Analytics: Leveraging Big Data for Optimization 1899.4 Data Privacy and Integrity 1909.5 Authentication and Access Control 1919.6 Intrusion Detection 1929.7 Adaptability to Evolving Threats 1939.8 The Role of Deep Learning in Security Enhancement 1949.8.1 Anomaly Detection with Deep Learning 1949.8.1.1 Unsupervised Learning: Detecting Novel Security Threats 1949.8.1.2 High-dimensional Data Processing: Learning Complex Patterns in Smart Environments 1959.8.2 Real-Time Analysis: Continuous Monitoring and Threat Detection 1969.8.3 Intrusion Detection Systems (IDSs) Using Deep Learning 1979.8.4 Adaptive Defense Mechanisms 1989.8.4.1 Self-learning Systems: Evolving with New Data 1989.8.4.2 Threat Prediction and Prevention: Anticipating Attacks Before They Happen 1999.8.4.3 Automated Response: Mitigating Threats Instantly 2009.8.4.4 Interdisciplinary Collaboration for Enhanced Security 2019.8.5 Cybersecurity and Machine Learning Experts: Designing Security Solutions for Smart Environments 2019.8.6 Device Manufacturers and IoT Developers: Building Security into Hardware and Software 2029.8.7 Ethics and Privacy Experts: Ensuring User Privacy and Trust 2039.8.8 Policymakers and Regulators: Establishing Standards and Regulations 2049.9 Challenges and Future Directions 2049.10 Conclusion 205References 20510 Deep Learning-Based Intrusion Detection in Wireless Networks 209Rahila Rahim and Mohammad Ahsan Chishti10.1 Introduction 20910.1.1 Mobile Ad Hoc Networks 21010.1.1.1 Components 21010.1.2 Wireless Sensor Networks (WSNs) 21210.2 The Importance of Security in Wireless Networks 21410.3 Challenges of Intrusion Detection Systems (IDS) in MANETs and WSNs 21510.3.1 Dynamic Topology and Resource Constraints 21510.3.2 Advanced Threats 21710.3.3 Flexibility and Distributed Processing 21710.3.4 Privacy Concerns 21710.3.5 Wireless Medium Weaknesses 21810.4 Intrusion Detection Systems 21810.5 Applications of Deep Learning in Bolstering Security Across Wireless Networks 22010.5.1 Neural Networks 22110.5.2 Artificial Neural Networks (ANNs) 22210.5.3 Conventional Neural Networks (CNNs) 22310.5.4 Recurrent Neural Networks (RNNs) 22310.5.5 Long Short-Term Memory 22510.6 Deep Learning-Based Solutions for the Challenges of Intrusion Detection in MANETs and WSNs 22810.6.1 Use Case for Autoencoders in Resource-Constrained Wireless Networks 22810.6.2 Use Cases of CNNs for DDoS Detection 229References 22911 Deep Learning-Based Intrusion Detection in Wireless Networks 233Shadab Alam and Sadaf Ahmad11.1 Introduction 23311.2 Wireless Network Security and IDS Challenges 23411.3 Deep Learning for Intrusion Detection 23511.3.1 Role of Deep Learning in IDS 23511.3.2 Enhancing IDS with Emerging Technologies 23511.4 Common Deep Learning Architectures for IDS 23611.4.1 Convolutional Neural Networks (CNNs) for IDSs 23611.4.2 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (lstm) 23611.4.3 Autoencoders for Anomaly Detection 23711.4.4 Generative Adversarial Networks (GANs) for IDSs 23711.4.5 Transformer-Based Models for Scalable IDS 23711.4.6 Comparative Analysis of Deep Learning Architectures 23711.5 Applications of Deep Learning-Based IDS 23811.5.1 IDSs in IoT Security 23811.5.1.1 Security Challenges in Resource-Constrained IoT Devices 23911.5.1.2 Deploying Lightweight Deep Learning Models on IoT Gateways 23911.5.1.3 Real-World Implementations 23911.5.2 IDSs in 5G and Beyond Networks 24011.5.2.1 Emerging Threats in 5G and Future Wireless Networks 24011.5.2.2 AI-Driven Network Slicing for Intrusion Detection 24011.5.2.3 Real-World Implementations 24111.5.3 IDS for Mobile Ad Hoc Networks (MANETs) 24111.5.3.1 Challenges of Decentralized and Dynamic MANET Security 24111.5.3.2 Deep Learning Techniques for Detecting Routing Attacks 24111.5.3.3 Real-World Implementations 24211.6 Challenges and Future Research Directions 24211.6.1 Adversarial Attacks Against Deep Learning IDS 24211.6.1.1 Techniques Used by Attackers to Evade IDSs 24211.6.1.2 Defensive Measures: Adversarial Training and Robust IDS Models 24311.6.2 Computational Overhead and Energy Efficiency 24311.6.2.1 Issues in Deploying Deep Learning Models on Resource-Constrained Devices 24311.6.2.2 Optimization Techniques: Model Pruning, Quantization, and Knowledge Distillation 24311.6.3 Real-Time Deployment Challenges 24411.6.4 Issues with Processing High-Speed Network Traffic 24411.6.4.1 Combining Deep Learning with Rule-Based and Hybrid IDSs 24411.6.5 Explainability and Interpretability of IDS Models 24411.6.5.1 Need for Trust and Transparency in AI-Driven Security 24511.6.5.2 Explainable AI (XAI) Approaches 24511.6.5.3 Benefits of XAI in IDSs 24511.6.6 Integration with Edge Computing and Federated Learning 24511.6.6.1 Distributed IDSs for Large-Scale, Decentralized Networks 24611.6.6.2 Privacy-Preserving AI for Intrusion Detection 24611.6.7 Future Directions in IDS Research 24611.6.7.1 Quantum AI for Cybersecurity 24611.6.7.2 AI-Driven Autonomous IDSs with Reinforcement Learning 24611.6.7.3 Blockchain-Based Intrusion Detection 24611.7 Conclusion 247References 24712 Securing IoT Environments: Deep Learning-Based Intrusion Detection 251Ashish K. Sharma, Neha Purohit, Shubhalaxmi Joshi, Itika Umesh Lakkewar, and Prashant Khobragade12.1 Introduction 25112.2 Overview of IoT Security Challenges 25312.3 Deep Learning for Intrusion Detection in IoT 25512.3.1 Implementation Examples 25612.4 Ensuring Data Safety and Privacy in Deep Learning-Based Intrusion Detection Systems (IDSs) 25612.4.1 Data Security Measures 25612.4.1.1 Encryption Techniques for IoT Data 25612.4.1.2 Role of Secure Communication Protocols 25712.4.2 Privacy Protection 25712.4.2.1 Differential Privacy Techniques in Data Sharing 25712.4.2.2 Edge Computing to Reduce Exposure of Sensitive Data 25812.4.3 Case Studies 25812.5 IoT Operations with Security Optimization 25912.5.1 Role of Intrusion Detection in Efficiency 25912.5.2 Bridge Between Security and Performance 25912.6 Challenges and Future Directions 26012.6.1 Current Limitations 26012.6.2 Research Opportunities 26012.6.3 Future Vision 26112.7 Conclusion 261References 26213 A Deep Learning Approach for the Detection of Zero-day Attacks 267Aamir S. Ahanger, Asra Khanam, Faheem Syeed Masoodi, and Bilal Ahmad Pandow13.1 Introduction 26713.2 Network Vulnerabilities 27113.3 Anomalies and Anomalies in Networks 27113.4 Deep Learning 27213.5 Vulnerabilities in Hardware and Software 27313.6 Network Configuration Vulnerabilities 27413.7 Network Hardware Vulnerabilities 27413.8 Network Perimeter Vulnerabilities 27513.9 Network Monitoring and Logging Vulnerabilities 27513.10 Communication Vulnerabilities 27513.11 Wireless Connection Vulnerabilities 27613.12 Cyberattacks That Exploit Vulnerabilities 27613.13 Denial of Service (DoS) 27713.14 User to Root (U2R) Attacks 27713.15 Remote to Local (R2L) Attacks 27813.16 Probe Attacks 27813.17 Deep Learning to Detect and Mitigate Zero-day Attacks 278References 280Index 285