Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware - to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security.
Author(s): Brij B. Gupta
Series: Advances in Information Security, Privacy, and Ethics
Publisher: Information Science Reference
Year: 2021
Language: English
Pages: 332
City: Hershey
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
