Advances in malware and data-driven network security - Gupta, Brij 1982- (IGI Global, 2021)
Includes bibliographical references and index
Chapter 1. Machine learning for malware analysis: methods, challenges, and future directions -- Chapter 2. Research trends for malware and intrusion detection on network systems: a topic modelling approach -- 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: authentication in wireless sensor networks (WSN) -- 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: a framework to detect cyberattacks revealing sensitive data in brain-computer interfaces -- 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. - Mode of access: World Wide Web