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.
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.
Audience
Research scholars and industry professionals in information technology, artificial intelligence and cybersecurity looking to innovate cybersecurity for cloud computing and IoT.
Table of Contents
Preface xvii
Part I: Intelligent Cloud-IoT Security 1
1 Intrusion Detection in Cloud-IoT Systems: Challenges and Opportunities 3
Anindita Raychaudhuri and Inadyuti Dutt
2 Applications of Artificial Intelligence for Early Detection of Cyber Threats in Cloud Networks for IoT Devices: A Sentinel Analysis 31
Kaushiki Chatterjee and Soumen Santra
3 Securing the Interconnected: AI-Driven Strategies for Dynamic Cloud-IoT Ecosystem 49
Ayan Banerjee and Anirban Kundu
4 Navigating the Fog AI-Driven Resilience and Privacy Preservation in Cloud IoT Environments 99
Bhupendra Panchal, Sarah Joby David, Ritika Singh, Manini Chhabra, Ajay Sharma and Tarannum Khan
5 Learning Safeguards: Leveraging Machine Learning for Anomaly Detection in Cloud – IoT Networks 119
Swastika Kayal and Soumen Santra
6 Smart Shields: Machine Learning Approaches for Adaptive Defense in Cloud-IoT Security 143
Bhupendra Panchal, Aafiya Choudhary, Ashish Anand, Ajay Sharma and Tarannum Khan
7 Real Time Threats Prediction and Security Issues in Cloud and Internet of Things System: The AI and ML Context 161
Nilanjan Das
8 Deep Learning Driven Heteromorphic Block Cipher (DL-HBC) Framework for Asynchronous Data Transmission in Heterogeneous Cloud Based Network 189
Nivedita Ray, Shreya Kumari, Ankita Bera, Shruti Singh and Anirban Kundu
Part II: Intelligent Intrusion Detection for Cloud-IoT System 225
9 Deep Learning Insights into Defending Against Adversarial Attacks in IoT Systems 227
J. Ramkumar and S. Vetrivel
10 Federated Learning for Intrusion Detection in Edge Computing for Cloud IoT Systems 251
Krupali Gosai, Hansa Vaghela, Yogeshwar Prajapati and Om Prakash Suthar
11 Behavioral Profiling for Dynamic Anomaly Detection in Cloud-IoT Networks 283
Triveni Lal Pal and Manoj Kumar Pandey
12 Immunity against Intrusion: Introducing an Agent-Based Blockchain Mechanism in Cloud IoT Environment 301
Amitabha Mandal and Pramit Ghosh
13 Designing a Hybrid Intrusion Detection System for Wireless Acoustic Sensor Networks: Enhancing Security During Audio Transmission 331
Utpal Ghosh and Uttam Kr. Mondal
References 350
Index 353
