"This book describes some of the recent notable advances in threat-detection using machine-learning and artificial-intelligence with a focus on malwares, covering the current trends in ML/statistical approaches to detecting, clustering or classification of cyber-threats extensively"--.

• Cover
• Title Page
• Copyright Page
• Book Series
• Dedication
• Editorial Advisory Board
• Table of Contents
• Detailed Table of Contents
• Preface
• Acknowledgment
• Chapter 1: Machine Learning for Malware Analysis
• Chapter 2: Research Trends for Malware and Intrusion Detection on Network Systems
• Chapter 3: Deep-Learning and Machine-Learning-Based Techniques for Malware Detection and Data-Driven Network Security
• Chapter 4: The Era of Advanced Machine Learning and Deep Learning Algorithms for Malware Detection
• Chapter 5: Malware Detection in Industrial Scenarios Using Machine Learning and Deep Learning Techniques
• Chapter 6: Malicious Node Detection Using Convolution Technique
• Chapter 7: Scalable Rekeying Using Linked LKH Algorithm for Secure Multicast Communication
• Chapter 8: Botnet Defense System and White-Hat Worm Launch Strategy in IoT Network
• Chapter 9: A Survey on Emerging Security Issues, Challenges, and Solutions for Internet of Things (IoTs)
• Chapter 10: SecBrain
• Chapter 11: A Study on Data Sharing Using Blockchain System and Its Challenges and Applications
• Chapter 12: Fruit Fly Optimization-Based Adversarial Modeling for Securing Wireless Sensor Networks (WSN)
• Chapter 13: Cybersecurity Risks Associated With Brain-Computer Interface Classifications
• Compilation of References
• About the Contributors
• Index