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For Authors
Submit a ProposalCyber Shields Using Artificial Intelligence
One Line Description
In an era where traditional firewalls and antivirus software no longer stand a chance against sophisticated cyberattacks, this book provides the essential roadmap you need to secure next-generation cloud, IoT, and decentralized systems against catastrophic data breaches and ransomware.
Description
Over the past few decades, traditional cybersecurity measures have proven insufficient against the ever-growing sophistication of cyberattacks. As technology advances, particularly with the proliferation of cloud computing, the Internet of Things, and decentralized applications, cybersecurity professionals are faced with new and more challenging security risks, ranging from data breaches to cyber-espionage and ransomware attacks. This book delves into the transformative impact of artificial intelligence and deep learning on contemporary cybersecurity practices. It provides a detailed exploration of how cutting-edge AI and deep learning methodologies are being leveraged to secure digital ecosystems, mitigate emerging threats, and strengthen cryptographic protocols. By integrating traditional security approaches with advanced machine learning and neural networks, the book offers readers a robust understanding of the next-generation tools and frameworks that are shaping the cybersecurity landscape. In addition to theoretical foundations, the book features practical case studies and real-world applications. Readers will gain insights into the use of AI for securing cloud environments, IoT networks, and mobile platforms. Emerging challenges such as securing AI models against adversarial attacks and ethical considerations in AI-driven cybersecurity are also discussed. With contributions from experts in academia and industry, the book offers a balanced perspective, making it a valuable resource for researchers, practitioners, and students at advanced undergraduate, graduate, and doctoral levels.
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Over the past few decades, traditional cybersecurity measures have proven insufficient against the ever-growing sophistication of cyberattacks. As technology advances, particularly with the proliferation of cloud computing, the Internet of Things, and decentralized applications, cybersecurity professionals are faced with new and more challenging security risks, ranging from data breaches to cyber-espionage and ransomware attacks. This book delves into the transformative impact of artificial intelligence and deep learning on contemporary cybersecurity practices. It provides a detailed exploration of how cutting-edge AI and deep learning methodologies are being leveraged to secure digital ecosystems, mitigate emerging threats, and strengthen cryptographic protocols. By integrating traditional security approaches with advanced machine learning and neural networks, the book offers readers a robust understanding of the next-generation tools and frameworks that are shaping the cybersecurity landscape. In addition to theoretical foundations, the book features practical case studies and real-world applications. Readers will gain insights into the use of AI for securing cloud environments, IoT networks, and mobile platforms. Emerging challenges such as securing AI models against adversarial attacks and ethical considerations in AI-driven cybersecurity are also discussed. With contributions from experts in academia and industry, the book offers a balanced perspective, making it a valuable resource for researchers, practitioners, and students at advanced undergraduate, graduate, and doctoral levels.
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Author / Editor Details
Shubham Mahajan, PhD is a Post Doctoral Reseacher in the Department of Applied Data Science at Noroff Univeristy College in Kristiansand, Norway. He holds eleven Indian patents, one Australian and one German patent and has published more than 70 articles in peer-reviewed journals and conferences. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication.
Shubham Mahajan, PhD is a Post Doctoral Reseacher in the Department of Applied Data Science at Noroff Univeristy College in Kristiansand, Norway. He holds eleven Indian patents, one Australian and one German patent and has published more than 70 articles in peer-reviewed journals and conferences. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication.
Kamal Upreti, PhD is an Associate Professor in the Department of Computer Science at Christ University), Ghaziabad, India. He has published more than 50 patents, 32 magazine issues, 110 research papers in journals and international conferences, and 45 books. His areas of interest include modern physics, data analytics, cyber security, machine learning, healthcare, and embedded systems.
Rashmi Agrawal, PhD is a Professor and Associate Dean at the School of Computer Applications at the Manav Rahna International Institute of Research and Studies, Faridabad, India with more than 23 years of experience. She has published more than 100 research papers in esteemed national and international journals and conferences and authored and co-authored books with distinguished publishers. Her research focuses on machine learning, classification, and pattern recognintion.
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Table of Contents
Preface
1. Emerging Trends in Artificial Intelligence for Cybersecurity
Utpal Ghosh and Shrabanti Kundu
1.1 Introduction
1.1.1 Historical Context and Motivation for AI in Cybersecurity
1.1.2 Understanding the Cyber Threat Landscape
1.1.3 Role of AI in Cybersecurity
1.1.4 Key AI Techniques in Cybersecurity
1.1.4.1 Machine Learning
1.1.4.2 Deep Learning
1.1.4.3 Reinforcement Learning
1.1.4.4 Transfer Learning
1.1.4.5 Zero-Shot Learning
1.1.5 Challenges and Limitations
1.1.6 Future Directions
1.2 Literature Review
1.2.1 Early Applications of AI in Cybersecurity
1.2.2 Evolution of ML Techniques in Cybersecurity
1.2.3 Emergence of DL in Cybersecurity
1.2.4 Rise of RL in Cybersecurity
1.2.5 Advent of TL and ZSL
1.2.6 Current Challenges and Research Gaps
1.2.6.1 Interpretability and Trust
1.2.6.2 Adversarial ML
1.2.6.3 Data Imbalance and Scarcity
1.2.6.4 Dynamic Threat
1.2.6.5 Ethical and Privacy
1.3 Methodology
1.3.1 Experimental Design
1.3.1.1 Model Selection and Implementation
1.3.1.2 Dataset Preprocessing and Feature Engineering
1.3.1.3 Training, Validation, and Testing
1.3.2 AI Models Utilized
1.3.2.1 ML Models
1.3.2.2 DL Models
1.3.2.3 RL Models
1.3.2.4 TL Models
1.3.2.5 Models for ZSL
1.3.3 Datasets
1.3.4 Evaluation Metrics
1.3.5 Cross-Validation and Hyperparameter Tuning
1.3.6 Ethical Considerations
1.4 Results and Comparative Analysis
1.4.1 Performance on Intrusion Detection Tasks
1.4.1.1 NSL-KDD Dataset
1.4.1.2 CICIDS 2017 Dataset
1.4.2 Performance on Malware Detection Tasks
1.4.2.1 EMBER 2018 Dataset
1.4.3 ZSL Results
1.4.4 Summary of Comparative Analysis
1.5 Discussion, Challenges, and Research Gap
1.5.1 Discussion
1.5.2 Challenges and Research Gaps
1.6 Conclusions
References
2. Deep Learning for Cybersecurity
Himani Tyagi, Aditya Dayal Tyagi and Swati Sah
2.1 Introduction
2.1.1 Overview of the Cybersecurity Landscape
2.1.2 Need for Automation and Intelligence in Threat Detection
2.1.3 Why DL for Cybersecurity?
2.2 Fundamentals of DL
2.2.1 Brief Introduction to DL
2.2.2 Common Architectures: CNN, RNN, LSTM, GANs, and Transformers
2.2.3 Convolutional Neural Networks
2.2.4 Recurrent Neural Networks
2.2.5 Long Short-Term Memory Networks
2.2.6 Generative Adversarial Networks
2.2.7 Transformers
2.2.8 Relevance of DL Characteristics to Cybersecurity Tasks
2.2.8.1 Automatic Feature Extraction
2.2.8.2 Scalability to Big Data
2.2.8.3 Adaptability and Generalization
2.2.8.4 Sequential and Temporal Pattern Recognition
2.2.8.5 Robustness to Noisy Data
2.2.8.6 Adversarial Learning and Simulation
2.2.8.7 Real-Time Detection and Decision-Making
2.3 DL Applications in Cybersecurity
2.3.1 Malware Detection
2.3.1.1 Static Analysis versus Dynamic Analysis
2.3.1.2 DL Techniques for Malware Classification
2.3.2 Intrusion Detection Systems
2.3.3 Phishing Detection
2.3.4 Spam and Botnet Detection
2.3.5 CTI Automation
2.3.6 Adversarial Attack Detection
2.3.7 User and Entity Behavior Analytics
2.4 DL Techniques and Architectures in Cybersecurity
2.4.1 CNN for Image-Based Malware
2.4.2 RNN and LSTM for Sequential Logs
2.4.3 Autoencoders for Anomaly Detection
2.4.4 GANs for Attack Simulation and Detection
2.4.5 Transformer Models for Cybersecurity Text Data
2.5 Challenges and Limitations
2.5.1 Adversarial ML
2.5.2 Interpretability and Explainability Issues
2.5.3 Data Scarcity and Data Imbalance
2.5.4 Computational Cost and Model Deployment Hurdles
2.5.5 Privacy Concerns in Cybersecurity Datasets
2.6 Emerging Trends
2.6.1 FL for Distributed Security Systems
2.6.2 SSL and Its Potential
2.6.3 XAI in Cybersecurity
2.6.4 ZTA with AI Assistance
2.6.5 Real-Time Threat Detection Using Edge AI
2.7 Case Studies
2.7.1 Real-World Applications of DL in Cybersecurity
2.7.2 Success Stories and Lessons Learned
2.7.3 Analysis of Some Well-Known Attacks and DL Responses
2.7.3.1 WannaCry Ransomware (2017)
2.7.3.2 SolarWinds Supply Chain Attack (2020)
2.7.3.3 Twitter Bitcoin Scam (2020)
2.7.4 Bridging the Gap between Research and Practice
2.7.5 Conclusion of Case Studies
2.8 Future Directions
2.8.1 Building Resilient AI Models against Adversarial Attacks
2.8.2 Human–AI Collaboration for Cybersecurity
2.8.3 Regulatory Frameworks and Ethical Considerations
2.9 Conclusion
2.9.1 Summary of Key Points
2.9.2 The Evolving Role of DL in Securing Digital Environments
Bibliography
3. Cloud and IoT Security with AI
Amandeep Kaur, Ramandeep Sandhu, Indu Rani, Gaganpreet Kaur and Deepika Ghai
3.1 Introduction
3.2 Integration of AI in People Analytics
3.2.1 Defining People Analytics
3.2.2 AI Technologies in People Analytics
3.2.3 Case Studies
3.2.4 Challenges and Ethical Considerations
3.3 AI-Driven Marketing Strategies
3.3.1 The Evolution of Marketing in the Digital Era
3.3.2 Utilizing Consumer Data with AI
3.3.3 Impact on Sales Performance
3.3.4 Ethical Marketing Practices
3.4 AI in Infrastructure Finance
3.4.1 Overview of Infrastructure Finance
3.4.2 AI-Assisted Risk Evaluation
3.4.3 Data-Driven Decision-Making in Finance
3.4.4 Challenges in Implementation
3.5 Security Challenges in Cloud and IoT Environments
3.5.1 Overview of Security Risks
3.5.2 AI-Driven Security Solutions
3.5.3 Data Intrusion and Authentication Issues
3.5.4 Privacy Concerns and Compliance
3.6 Integrating AI Security Mechanisms
3.6.1 Framework for AI Security in Business Operations
3.6.2 Best Practices for Implementation
3.6.3 Future Trends in AI Security
3.7 Literature Review and Current Trends
3.7.1 Overview of Existing Research
3.7.2 Modern Business Trends
3.7.3 Security Obstacles and Prevention Methods
3.8 Conclusion and Recommendations
3.8.1 Summary of Key Insights
3.8.2 Implications for Businesses and Policymakers
3.8.3 Future Research Directions
References
4. Reinforcement and Deep Learning Approaches to Dynamic
Cache Optimization
Patel Smit Vasant Kumar, Kruti Dataram, Uma Shankar, Sonam Nagpal and Himanshu Amritlal Patel
4.1 Introduction
4.1.1 Traditional Cache Management Techniques and Their Limitations
4.2 Fundamentals of Cache Management
4.2.1 Cache Memory Architecture (L1, L2, L3)
4.2.2 Caching Policies (LRU, LFU, FIFO)
4.2.3 Key Performance Metrics (Hit Ratio, Latency, Throughput)
4.2.4 Challenges in Cache Management in Modern Systems (e.g., Mobile, Cloud, Edge Devices)
4.3 Role of AI in Systems Optimization
4.3.1 AI Techniques Overview
4.4 ML Approaches to Cache Management
4.4.1 Predictive Modeling for Access Patterns
4.4.2 Feature Selection and Importance (e.g., Memory Address Sequences, Access Time)
4.4.3 Evaluation Framework and Datasets (SPEC Benchmarks, Real-World Traces)
4.5 RL for Adaptive Cache Policies
4.5.1 Q-Learning and DQNs for Cache Decisions
4.5.2 Reward Design: Hit/Miss Tradeoffs and Energy Efficiency
4.5.3 Simulation Environment and State Representation
4.5.4 Case Studies and Implementations
4.6 DL in Cache Prefetching and Replacement
4.6.1 Sequence Modeling with Recurrent Neural Networks/Long Short-Term Memorys for Prefetching
4.6.2 Autoencoders for Cache Content Compression
4.6.3 Transformers in Cache Prediction Tasks
4.7 AI in Edge, Cloud, and Multicore Cache Environments
4.7.1 Cache Challenges in Distributed and Heterogeneous Systems
4.7.2 Federated Learning for Cache Management in IoT/Edge
4.7.3 AI in Multicore Cache Coherency Management
References
5. ARKANA: Extracting Knowledge Using Deep Learning for Intrusion Detection From Data Streams
Aravindan V., Rajkanwar Singh, Sanket Mishra and Sandipan Maiti
5.1 Introduction
5.2 Literature Review
5.3 Methodology
5.3.1 Data Ingestion and Pipeline
5.3.2 Data Resampling
5.3.3 Feature Selection
5.3.4 Classifiers
5.3.5 Ranking Algorithm
5.3.6 Serving
5.4 Evaluation Metrics
5.5 Results and Discussion
5.5.1 CICEVSE Dataset
5.5.2 ToN_IoT Dataset
5.6 Conclusion
References
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Preface
1. Emerging Trends in Artificial Intelligence for Cybersecurity
Utpal Ghosh and Shrabanti Kundu
1.1 Introduction
1.1.1 Historical Context and Motivation for AI in Cybersecurity
1.1.2 Understanding the Cyber Threat Landscape
1.1.3 Role of AI in Cybersecurity
1.1.4 Key AI Techniques in Cybersecurity
1.1.4.1 Machine Learning
1.1.4.2 Deep Learning
1.1.4.3 Reinforcement Learning
1.1.4.4 Transfer Learning
1.1.4.5 Zero-Shot Learning
1.1.5 Challenges and Limitations
1.1.6 Future Directions
1.2 Literature Review
1.2.1 Early Applications of AI in Cybersecurity
1.2.2 Evolution of ML Techniques in Cybersecurity
1.2.3 Emergence of DL in Cybersecurity
1.2.4 Rise of RL in Cybersecurity
1.2.5 Advent of TL and ZSL
1.2.6 Current Challenges and Research Gaps
1.2.6.1 Interpretability and Trust
1.2.6.2 Adversarial ML
1.2.6.3 Data Imbalance and Scarcity
1.2.6.4 Dynamic Threat
1.2.6.5 Ethical and Privacy
1.3 Methodology
1.3.1 Experimental Design
1.3.1.1 Model Selection and Implementation
1.3.1.2 Dataset Preprocessing and Feature Engineering
1.3.1.3 Training, Validation, and Testing
1.3.2 AI Models Utilized
1.3.2.1 ML Models
1.3.2.2 DL Models
1.3.2.3 RL Models
1.3.2.4 TL Models
1.3.2.5 Models for ZSL
1.3.3 Datasets
1.3.4 Evaluation Metrics
1.3.5 Cross-Validation and Hyperparameter Tuning
1.3.6 Ethical Considerations
1.4 Results and Comparative Analysis
1.4.1 Performance on Intrusion Detection Tasks
1.4.1.1 NSL-KDD Dataset
1.4.1.2 CICIDS 2017 Dataset
1.4.2 Performance on Malware Detection Tasks
1.4.2.1 EMBER 2018 Dataset
1.4.3 ZSL Results
1.4.4 Summary of Comparative Analysis
1.5 Discussion, Challenges, and Research Gap
1.5.1 Discussion
1.5.2 Challenges and Research Gaps
1.6 Conclusions
References
2. Deep Learning for Cybersecurity
Himani Tyagi, Aditya Dayal Tyagi and Swati Sah
2.1 Introduction
2.1.1 Overview of the Cybersecurity Landscape
2.1.2 Need for Automation and Intelligence in Threat Detection
2.1.3 Why DL for Cybersecurity?
2.2 Fundamentals of DL
2.2.1 Brief Introduction to DL
2.2.2 Common Architectures: CNN, RNN, LSTM, GANs, and Transformers
2.2.3 Convolutional Neural Networks
2.2.4 Recurrent Neural Networks
2.2.5 Long Short-Term Memory Networks
2.2.6 Generative Adversarial Networks
2.2.7 Transformers
2.2.8 Relevance of DL Characteristics to Cybersecurity Tasks
2.2.8.1 Automatic Feature Extraction
2.2.8.2 Scalability to Big Data
2.2.8.3 Adaptability and Generalization
2.2.8.4 Sequential and Temporal Pattern Recognition
2.2.8.5 Robustness to Noisy Data
2.2.8.6 Adversarial Learning and Simulation
2.2.8.7 Real-Time Detection and Decision-Making
2.3 DL Applications in Cybersecurity
2.3.1 Malware Detection
2.3.1.1 Static Analysis versus Dynamic Analysis
2.3.1.2 DL Techniques for Malware Classification
2.3.2 Intrusion Detection Systems
2.3.3 Phishing Detection
2.3.4 Spam and Botnet Detection
2.3.5 CTI Automation
2.3.6 Adversarial Attack Detection
2.3.7 User and Entity Behavior Analytics
2.4 DL Techniques and Architectures in Cybersecurity
2.4.1 CNN for Image-Based Malware
2.4.2 RNN and LSTM for Sequential Logs
2.4.3 Autoencoders for Anomaly Detection
2.4.4 GANs for Attack Simulation and Detection
2.4.5 Transformer Models for Cybersecurity Text Data
2.5 Challenges and Limitations
2.5.1 Adversarial ML
2.5.2 Interpretability and Explainability Issues
2.5.3 Data Scarcity and Data Imbalance
2.5.4 Computational Cost and Model Deployment Hurdles
2.5.5 Privacy Concerns in Cybersecurity Datasets
2.6 Emerging Trends
2.6.1 FL for Distributed Security Systems
2.6.2 SSL and Its Potential
2.6.3 XAI in Cybersecurity
2.6.4 ZTA with AI Assistance
2.6.5 Real-Time Threat Detection Using Edge AI
2.7 Case Studies
2.7.1 Real-World Applications of DL in Cybersecurity
2.7.2 Success Stories and Lessons Learned
2.7.3 Analysis of Some Well-Known Attacks and DL Responses
2.7.3.1 WannaCry Ransomware (2017)
2.7.3.2 SolarWinds Supply Chain Attack (2020)
2.7.3.3 Twitter Bitcoin Scam (2020)
2.7.4 Bridging the Gap between Research and Practice
2.7.5 Conclusion of Case Studies
2.8 Future Directions
2.8.1 Building Resilient AI Models against Adversarial Attacks
2.8.2 Human–AI Collaboration for Cybersecurity
2.8.3 Regulatory Frameworks and Ethical Considerations
2.9 Conclusion
2.9.1 Summary of Key Points
2.9.2 The Evolving Role of DL in Securing Digital Environments
Bibliography
3. Cloud and IoT Security with AI
Amandeep Kaur, Ramandeep Sandhu, Indu Rani, Gaganpreet Kaur and Deepika Ghai
3.1 Introduction
3.2 Integration of AI in People Analytics
3.2.1 Defining People Analytics
3.2.2 AI Technologies in People Analytics
3.2.3 Case Studies
3.2.4 Challenges and Ethical Considerations
3.3 AI-Driven Marketing Strategies
3.3.1 The Evolution of Marketing in the Digital Era
3.3.2 Utilizing Consumer Data with AI
3.3.3 Impact on Sales Performance
3.3.4 Ethical Marketing Practices
3.4 AI in Infrastructure Finance
3.4.1 Overview of Infrastructure Finance
3.4.2 AI-Assisted Risk Evaluation
3.4.3 Data-Driven Decision-Making in Finance
3.4.4 Challenges in Implementation
3.5 Security Challenges in Cloud and IoT Environments
3.5.1 Overview of Security Risks
3.5.2 AI-Driven Security Solutions
3.5.3 Data Intrusion and Authentication Issues
3.5.4 Privacy Concerns and Compliance
3.6 Integrating AI Security Mechanisms
3.6.1 Framework for AI Security in Business Operations
3.6.2 Best Practices for Implementation
3.6.3 Future Trends in AI Security
3.7 Literature Review and Current Trends
3.7.1 Overview of Existing Research
3.7.2 Modern Business Trends
3.7.3 Security Obstacles and Prevention Methods
3.8 Conclusion and Recommendations
3.8.1 Summary of Key Insights
3.8.2 Implications for Businesses and Policymakers
3.8.3 Future Research Directions
References
4. Reinforcement and Deep Learning Approaches to Dynamic
Cache Optimization
Patel Smit Vasant Kumar, Kruti Dataram, Uma Shankar, Sonam Nagpal and Himanshu Amritlal Patel
4.1 Introduction
4.1.1 Traditional Cache Management Techniques and Their Limitations
4.2 Fundamentals of Cache Management
4.2.1 Cache Memory Architecture (L1, L2, L3)
4.2.2 Caching Policies (LRU, LFU, FIFO)
4.2.3 Key Performance Metrics (Hit Ratio, Latency, Throughput)
4.2.4 Challenges in Cache Management in Modern Systems (e.g., Mobile, Cloud, Edge Devices)
4.3 Role of AI in Systems Optimization
4.3.1 AI Techniques Overview
4.4 ML Approaches to Cache Management
4.4.1 Predictive Modeling for Access Patterns
4.4.2 Feature Selection and Importance (e.g., Memory Address Sequences, Access Time)
4.4.3 Evaluation Framework and Datasets (SPEC Benchmarks, Real-World Traces)
4.5 RL for Adaptive Cache Policies
4.5.1 Q-Learning and DQNs for Cache Decisions
4.5.2 Reward Design: Hit/Miss Tradeoffs and Energy Efficiency
4.5.3 Simulation Environment and State Representation
4.5.4 Case Studies and Implementations
4.6 DL in Cache Prefetching and Replacement
4.6.1 Sequence Modeling with Recurrent Neural Networks/Long Short-Term Memorys for Prefetching
4.6.2 Autoencoders for Cache Content Compression
4.6.3 Transformers in Cache Prediction Tasks
4.7 AI in Edge, Cloud, and Multicore Cache Environments
4.7.1 Cache Challenges in Distributed and Heterogeneous Systems
4.7.2 Federated Learning for Cache Management in IoT/Edge
4.7.3 AI in Multicore Cache Coherency Management
References
5. ARKANA: Extracting Knowledge Using Deep Learning for Intrusion Detection From Data Streams
Aravindan V., Rajkanwar Singh, Sanket Mishra and Sandipan Maiti
5.1 Introduction
5.2 Literature Review
5.3 Methodology
5.3.1 Data Ingestion and Pipeline
5.3.2 Data Resampling
5.3.3 Feature Selection
5.3.4 Classifiers
5.3.5 Ranking Algorithm
5.3.6 Serving
5.4 Evaluation Metrics
5.5 Results and Discussion
5.5.1 CICEVSE Dataset
5.5.2 ToN_IoT Dataset
5.6 Conclusion
References
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