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.
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
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.
