Search

Browse Subject Areas

For Authors

Submit a Proposal

Strategic Approaches to Intrusion Detection in Cloud-IoT Ecosystem

Edited by Partha Ghosh, Rajdeep Chakraborty, Anupam Ghosh and Ahmed A. Elngar
Series: Advances in Learning Analytics for Intelligent Cloud-IoT Systems
Copyright: 2026   |   Expected Pub Date:2026/03/30
ISBN: 9781394341948  |  Hardcover  |  
379 pages

One Line Description
Future-proof your digital infrastructure with this essential book, which provides a comprehensive exploration of both traditional and advanced machine and deep learning models to implement resilient and intelligent intrusion detection systems for securing complex cloud-IoT environments.

Audience
Research scholars and industry professionals in information technology, artificial intelligence and cybersecurity looking to innovate cybersecurity for cloud computing and IoT.

Description
The rapid growth of cloud computing and the Internet of Things has transformed industry by enabling real-time data collection, processing, and automation. However, this increasing interconnectivity also introduces significant security challenges, including data breaches, unauthorized access, and cyber threats. Ensuring the security and privacy of cloud-IoT environments requires advanced intrusion detection mechanisms, privacy-preserving strategies, and efficient resource management. This book explores various advanced methods to achieve these goals, including machine and deep learning models, to protect cloud-IoT systems against cyber threats. This book covers both traditional and advanced techniques to implement intrusion detection systems and provides detailed comparative analysis. By offering practical insights, readers will gain a deeper understanding of how to effectively implement intelligent security solutions, ensuring resilience, privacy, and protection against evolving cyber threats in cloud-IoT environments.
Readers will find the volume:
• Provides comprehensive coverage of topics like machine and deep learning for intelligent security;
• Explores cyber-IoT systems and intrusion detection systems for identifying suspicious activities and mitigating potential threats;
• Discusses various security mechanisms to safeguard the cloud-IoT environment and implement various techniques to detect intrusions early on.

Back to Top
Author / Editor Details
Partha Ghosh, PhD is an Associate Professor in the Department of Information Technology and the Head of the Department of Computer Science and Business Systems at the Netaji Subhash Engineering College, Kolkata, India. He has published more than 20 research papers in reputed journals and conferences. His research interests include cloud computing, machine learning, intrusion detection systems, optimization techniques, feature selection, computer networks, and security.

Rajdeep Chakraborty, PhD is a Professor in the Computer Science and Engineering Department at Medi-Caps University, Indore, Madhya Pradesh, India with nearly two decades of research and teaching experience. He has made notable contributions through various publications, including patents, books, journal articles, and conference papers. His research interests include cryptography, network security, cybersecurity, IoT, and blockchain.

Anupam Ghosh, PhD is a Professor and Head of the Department of Computer Science and Engineering at Netaji Subhash Engineering College, Kolkata, India with more than 22 years of experience. He has published more than 100 international papers in reputed journals and conferences. His research focuses on AI, machine learning, deep learning, image processing, soft computing, and bioinformatics.

Ahmed A. Elngar, PhD is an Associate Professor and Head of the Computer Science Department in the School of Computers and Artificial Intelligence at Beni-Suef University, Egypt and an Associate Professor of Computer Science in the College of Computer Information Technology at American University in the United Arab Emirates. He has published more than 150 scientific research papers in prestigious international journals and more than 35 books. His research interests include the Internet of Things, network security, intrusion detection, machine learning, data mining, and artificial intelligence.

Back to Top

Table of Contents
Preface
Part I: Intelligent Cloud-IoT Security
1. Intrusion Detection in Cloud-IoT Systems: Challenges and Opportunities
Anindita Raychaudhuri and Inadyuti Dutt
1.1 Introduction
1.2 Overview of Cloud IoT Systems
1.3 Challenges in Cloud IoT Systems
1.4 Security Issues in Cloud Systems
1.5 Evolution of Intrusion Detection Systems
1.5.1 Evolution of IDTs in IoT-Cloud Systems
1.5.2 Comparative Analysis of Intrusion Detection Systems
1.6 Techniques and Algorithms for Intrusion Detection
1.7 Applications Areas of Intrusion Detection in Cloud-IoT Systems
1.8 Future Directions and Research Opportunities
1.9 Conclusion
References
2. Applications of Artificial Intelligence for Early Detection of Cyber Threats in Cloud Networks for IoT Devices: A Sentinel Analysis
Kaushiki Chatterjee and Soumen Santra
2.1 Introduction
2.2 Implementing Protective Measures and Following Best Practices to Mitigate Threats from IoCST
2.3 Utilizing Diffie-Hellman for Enhancing IoT Security
2.4 Utilizing Machine Learning to Enhance Security in the Realm of IoT
2.5 Future
2.6 Conclusion
References
3. Securing the Interconnected: AI-Driven Strategies for Dynamic Cloud-IoT Ecosystem
Ayan Banerjee and Anirban Kundu
3.1 Introduction
3.1.1 Overview
3.1.2 Aim
3.1.3 Scope
3.1.4 Motivation
3.1.5 Organization
3.2 Literature Review
3.2.1 Past Researches
3.2.2 Challenges
3.3 CA Based MCMS Framework for System Allocation Using Memory Capacity Analysis
3.4 Cloud-Based Communication between Administrator Module and Controller Module for Maintaining IoT Ecosystem Capacity
3.5 Functional Communication between User Module and Controller Module for Query Analysis
3.6 Controller Design for Measuring System Capacity Using CA
3.6.1 CA Based Controller Design for IoT Ecosystems Performance Sustainability
3.6.2 CA Based Controller Design for User Query Analysis
3.7 Analytical Discussion
3.7.1 Connection Demand Analysis Based on Connections between Web Server and Database Server
3.7.2 Server Load Analysis Based on Connections between Web Server and Database Server
3.7.3 HDD Capacity Analysis
3.7.4 RAM Capacity Analysis
3.7.5 Memory Capacity Analysis
3.7.6 System Reliability Analysis
3.8 Theoretical Discussion
3.8.1 Theoretical Perspective on Server Load Evaluation
3.8.2 Theoretical Examination of HDD Capacity Analysis
3.8.3 Theoretical Examination of RAM Capacity Analysis
3.8.4 Theoretical Foundation on Memory Capacity Analysis
3.8.5 Theoretical Discussion on System Reliability
3.9 Experimental Discussion
3.9.1 Overview
3.9.2 Experimental Setup
3.9.3 Time Complexity Analysis
3.9.4 System Load Analysis
3.9.5 System Proficiency Analysis Using Different Factors
3.10 Comparison
3.11 Conclusion
Acknowledgment
References
4. Navigating the Fog AI-Driven Resilience and Privacy Preservation in Cloud IoT Environments
Bhupendra Panchal, Sarah Joby David, Ritika Singh, Manini Chhabra, Ajay Sharma and Tarannum Khan
4.1 Introduction
4.2 Literature Review
4.2.1 Cloud and IoT: Challenges and Opportunities
4.2.2 AI-Driven Resilience in Fog and Cloud IoT Environments
4.2.3 Privacy Preservation in AI-Driven Cloud IoT Systems
4.2.4 Security Concerns and AI Mitigation Strategies
4.3 Proposed Work
4.4 Experimental Setup
4.4.1 Tools
4.4.2 Simulation
4.4.3 Dataset
4.5 Experimental Results
4.5.1 Privacy Breach Risk Comparison
4.5.2 Latency Comparison
4.5.3 Bandwidth Usage Comparison
4.5.4 Model Accuracy and Resilience Comparison
4.6 Conclusion
References
5. Learning Safeguards: Leveraging Machine Learning for Anomaly Detection in Cloud – IoT Networks
Swastika Kayal and Soumen Santra
5.1 Introduction
5.1.1 Cloud Security
5.1.2 Adhoc Network
5.2 Background and Literature Survey
5.3 Methodology
5.3.1 Deviation Detection System
5.3.1.1 Anomaly Detection in Network Using Optimized Kernel-SVM
5.3.1.2 Anomaly Detection in Network Using Hierarchical Trees
5.3.2 Intrusion Detection System
5.3.3 Behavioral Malware Detection Techniques
5.3.4 Bayesian Network for Predictive Threat Modeling
5.4 Comparative Analysis
5.4.1 Comparative Analysis of Outlier Detection Techniques
5.4.2 Supervised Learning: Kernel SVM
5.4.2.1 Pros
5.4.2.2 Cons
5.4.3 Supervised Learning: Hierarchical Trees
5.4.3.1 Pros
5.4.3.2 Cons
5.4.4 Deep Learning: Spatial Feature Learner (SFL)
5.4.4.1 Pros
5.4.4.2 Cons
5.4.5 Deep Learning: Recurrent Neural Networks (RNN)
5.4.5.1 Pros
5.4.5.2 Cons
5.4.6 Bayesian Networks for Predictive Threat Modeling
5.4.6.1 Pros
5.4.6.2 Cons
5.5 Results and Discussion
5.5.1 Dataset Link
5.5.2 Dataset Table
5.5.3 Output
5.6 Future Work
5.6.1 Transfer Learning in IoT Anomaly Detection
5.6.2 Semi-Supervised Learning for IoT
5.6.3 Data Augmentation Techniques for IoT Networks
5.6.4 Continuous Learning and Adaptation
5.6.5 Scalability and Real-Time Detection
5.7 Conclusion
References
6. Smart Shields: Machine Learning Approaches for Adaptive Defense in Cloud-IoT Security
Bhupendra Panchal, Aafiya Choudhary, Ashish Anand, Ajay Sharma and Tarannum Khan
6.1 Introduction
6.1.1 Motivation of the Study
6.1.2 Problem Statement
6.2 Literature Review
6.3 Proposed Methodology
6.3.1 Data Collection and Simulation
6.3.2 Layered Architecture
6.3.3 Model Adaptation and Defense Mechanisms
6.4 Experimental Result
6.4.1 Hardware and Network Environment
6.4.2 Datasets
6.4.3 ML Algorithms
6.4.4 Threat Simulation
6.4.5 Adaptive Defense Mechanism
6.5 Result Analysis
6.5.1 Detection Accuracy
6.5.2 Latency
6.5.3 Power Consumption
6.5.4 Model Scalability
6.5.5 Adaptability
6.6 Conclusion
References
7. Real Time Threats Prediction and Security Issues in Cloud and Internet of Things System: The AI and ML Context
Nilanjan Das
7.1 Introduction
7.2 Objectives
7.3 Methodology
7.4 Fundamentals of Cyber Security Issues
7.5 Fundamentals of IoT in Association with Cloud Computing
7.6 Foundation of Artificial Intelligence and Machine Learning
7.7 Cyber Threats and Intrusion Detection Using AI and ML
7.8 Real Time Threat Detection and Prediction on Cloud IoT Platform in the Context of Artificial Intelligence
7.9 Core Findings
7.10 Conclusion and Future Work
Acknowledgement
References
8. Deep Learning Driven Heteromorphic Block Cipher (DL-HBC) Framework for Asynchronous Data Transmission in Heterogeneous Cloud Based Network
Nivedita Ray, Shreya Kumari, Ankita Bera, Shruti Singh and Anirban Kundu
8.1 Introduction
8.1.1 Overview
8.1.2 Literature Survey
8.1.3 Aim
8.1.4 Scope
8.1.5 Motivation
8.1.6 Organization
8.2 System Design and Architecture for Heteromorphic DLE
8.3 Procedure for Heteromorphic DLE
8.4 Detailed Procedural Explanation for Design Framework
8.5 Analysis on Asynchronous Data Transmission
8.6 Experimental Observations
8.6.1 Experimental Setup
8.6.2 Experimental Results
8.6.3 Comparative Analysis
8.6.4 Cost Analysis
8.7 Conclusion
Acknowledgment
References
Part II: Intelligent Intrusion Detectionfor Cloud-IoT System
9. Deep Learning Insights into Defending Against Adversarial Attacks in IoT Systems
J. Ramkumar and S. Vetrivel
9.1 Introduction
9.1.1 Overview of Adversarial Attacks on IoT Systems
9.1.2 Role of Deep Learning in Enhancing IoT Security
9.1.3 Review Literature Nature of Adversarial Attacks
9.1.4 Definition and Characteristics
9.1.5 Common Techniques Used in Attacks
9.1.6 Impact on IoT Systems and Devices
9.2 IoT System Vulnerabilities
9.2.1 Security Flaws in IoT Devices
9.2.2 Network Vulnerabilities
9.2.3 Exploitation Methods and Scenarios
9.3 Deep Learning Approaches
9.3.1 Overview of Deep Learning Models
9.3.2 Specific Algorithms for Security
9.3.3 Training and Validation of Models
9.4 Defense Mechanisms
9.4.1 Detection of Adversarial Attacks
9.4.2 Real-Time Threat Response
9.4.3 Mitigation and Prevention Strategies
9.5 Integration with IoT Security Frameworks
9.5.1 System Design Considerations
9.5.2 Scalability and Performance Issues
9.5.3 Practical Implementation Steps
9.6 Recent Advances and Future Trends
9.6.1 Innovations in Deep Learning for Security
9.6.2 Future Research Directions
9.7 Conclusion
9.7.1 Key Takeaways
9.7.2 Implications for IoT Security and Deep Learning Applications
References
10. Federated Learning for Intrusion Detection in Edge Computing for Cloud IoT Systems
Krupali Gosai, Hansa Vaghela, Yogeshwar Prajapati and Om Prakash Suthar
10.1 Introduction
10.1.1 Overview of Cloud IoT Systems
10.1.2 Role of Edge Computing in IoT
10.1.3 Importance of Intrusion Detection
10.1.4 Federated Learning: A Decentralized Approach
10.2 Background
10.2.1 Related Work
10.2.1.1 Signature-Based Detection
10.2.1.2 Anomaly-Based Detection
10.2.1.3 Rule-Based Detection
10.2.2 Limitations of Centralized Intrusion Detection in IoT
10.2.3 Federated Learning for Security Applications
10.2.3.1 Federated Learning: Benefits for IoT Intrusion Detection
10.2.3.2 Challenges of Federated Learning in IoT Security
10.2.4 Comparative Analysis of Federated Learning and Traditional Machine Learning in Security
10.3 Federated Learning in Edge Computing for Intrusion Detection
10.3.1 Overview of Federated Learning
10.3.2 Architecture of Federated Learning for Edge Computing
10.3.3 Federated Learning Workflow for Intrusion Detection
10.4 Challenges and Solutions
10.4.1 Data Privacy and Security
10.4.2 Communication Overhead and Bandwidth Efficiency
10.4.3 Model Training Efficiency and Accuracy
10.4.4 Scalability in Large-Scale IoT Networks
10.5 Proposed Intrusion Detection Framework Using Federated Learning
10.5.1 Framework Design and Architecture
10.5.2 Model Selection and Training Processes
10.5.3 Model Synchronization and Data Combination
10.5.4 Federated Intrusion Detection Edge to Cloud Data Flow for Enhanced Security
10.6 Implementation and Experimentation
10.6.1 Experimental Setup
10.6.2 Data Collection and Preprocessing
10.6.3 Model Training and Evaluation Metrics
10.6.4 Performance Evaluation and Findings
10.7 Case Study: Real World Application of Federated Intrusion Detection
10.7.1 Case Study Background and Objectives
10.7.1.1 Case Study: Enhancing Cybersecurity in Financial Sector with Federated
Intrusion Detection
10.7.1.2 Case Study: Securing the Smart Grid with Federated Intrusion Detection
10.7.2 Implementation Details
10.8 Discussion
10.8.1 Enhanced Privacy
10.8.2 Improved Security
10.8.3 Overcoming IoT-Specific Challenges
10.8.4 Special Applications of Security in IoT
10.8.5 Challenges and Considerations
10.9 Future Directions
10.9.1 Advanced Federated Learning Techniques for IoT Security
10.9.2 Integrating Blockchain for Decentralized Authentication
10.9.3 AI in Anomaly Detection
10.10 Conclusion
References
11. Behavioral Profiling for Dynamic Anomaly Detection in Cloud-IoT Networks
Triveni Lal Pal and Manoj Kumar Pandey
11.1 Introduction
11.1.1 Real Motivation
11.1.2 Various Challenges in Securing Cloud-IoT Networks
11.1.3 Objectives and Scope of Behavioral Profiling
11.1.4 Organization of the Chapter
11.2 Cloud IoT Architecture
11.3 Literature Study
11.3.1 Anomaly Detection Techniques
11.3.2 Anomaly Detection in Cloud-IoT Network
11.3.3 Machine Learning Based Anomaly Detection
11.4 Emerging Trends and Opportunities
11.5 Conclusion and Future Direction
References
12. Immunity against Intrusion: Introducing an Agent-Based Blockchain Mechanism in Cloud IoT Environment
Amitabha Mandal and Pramit Ghosh
12.1 Introduction
12.1.1 Evolution of Digital System
12.1.2 Distributed Sensor Environment
12.1.3 Intrusion and Intrusion Detection
12.1.4 Internet of Things (IoT)
12.1.5 Cloud IoT
12.1.6 Blockchain
12.2 Contribution of the Authors
12.3 Proposed Agent-Based Blockchain Mechanism in Cloud IoT [ABBM Cloud IoT]
12.3.1 Proposed Scheme
12.3.2 Phase I: Device Registration
12.3.3 Phase II: Authentication with Key Management
12.3.4 Incorporating Blockchain in Key Management
12.4 Results and Discussion
12.4.1 Security Analysis
12.4.2 Overhead Metrics
12.4.2.1 Computation Cost
12.4.2.2 Communication Cost
12.4.2.3 Storage Cost
12.4.3 Blockchain Efficiency
12.4.3.1 Transaction Handling
12.4.3.2 Block Preparation Time
12.4.4 Summary of Results
12.5 Conclusion
References
13. Designing a Hybrid Intrusion Detection System for Wireless Acoustic Sensor Networks: Enhancing Security During Audio Transmission
Utpal Ghosh and Uttam Kr. Mondal
13.1 Introduction
13.2 Background
13.3 Proposed Hybrid IDS Architecture
13.3.1 Data Collection
13.3.2 Data Preprocessing
13.3.3 Signature-Based Detection
13.3.4 Anomaly-Based Detection
13.3.5 Machine Learning-Based Detection
13.3.6 Alert Generation
13.3.7 Incident Response
13.4 Experimental Setup
13.4.1 Simulation Environment
13.4.2 Network Topology
13.4.3 Audio Signal Characteristics
13.4.4 Hybrid Intrusion Detection System (HIDS) Configuration
13.4.5 Attack Scenarios
13.4.5.1 Scenario 1
13.4.5.2 Scenario 2
13.4.5.3 Scenario 3
13.4.6 Performance Metrics
13.4.7 Simulation Duration
13.4.8 Datasets
13.4.9 Training
13.5 Results Analysis and Performance Evaluation
13.5.1 Experimental Results
13.5.2 Comparative Performance Analysis
13.6 Conclusions and Future Scope
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page