Strategic Approaches to Intrusion Detection in Cloud-IoT Ecosystem.

Format:

Book

Author/Creator:

Ghosh, Partha.

Series:

Advances in Learning Analytics for Intelligent Cloud-IoT Systems Series

Language:

English

Subjects (All):

Intrusion detection systems (Computer security).

Cloud computing.

Physical Description:

1 online resource (380 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

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.

Contents:

Cover

Series Page

Title Page

Copyright Page

Contents

Preface

Part I: Intelligent Cloud-IoT Security

Chapter 1 Intrusion Detection in Cloud-IoT Systems: Challenges and Opportunities

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

Chapter 2 Applications of Artificial Intelligence for Early Detection of Cyber Threats in Cloud Networks for IoT Devices: A Sentinel Analysis

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

Chapter 3 Securing the Interconnected: AI-Driven Strategies for Dynamic Cloud-IoT Ecosystem

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 Ecosystem's 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

Chapter 4 Navigating the Fog AI-Driven Resilience and Privacy Preservation in Cloud IoT Environments

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

Chapter 5 Learning Safeguards: Leveraging Machine Learning for Anomaly Detection in Cloud - IoT Networks

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

Chapter 6 Smart Shields: Machine Learning Approaches for Adaptive Defense in Cloud-IoT Security

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

Chapter 7 Real Time Threats Prediction and Security Issues in Cloud and Internet of Things System: The AI and ML Context

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

Chapter 8 Deep Learning Driven Heteromorphic Block Cipher (DL-HBC) Framework for Asynchronous Data Transmission in Heterogeneous Cloud Based Network

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

Part II: Intelligent Intrusion Detection for Cloud-IoT System

Chapter 9 Deep Learning Insights into Defending Against Adversarial Attacks in IoT Systems

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

Chapter 10 Federated Learning for Intrusion Detection in Edge Computing for Cloud IoT Systems

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.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-394-34197-0

1-394-34196-2

9781394341962

OCLC:

1577382725