Advances in Malware and Data-Driven Network Security
Advances in Malware and Data-Driven Network SecurityНазвание: Advances in Malware and Data-Driven Network Security
Автор: Brij B. Gupta
Издательство: Information Science Reference, IGI Global
Год: 2022
Страниц: 326
Язык: английский
Формат: pdf (true), epub
Размер: 31.3 MB

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.

This book contains chapters dealing with different aspects of Malware analysis, Intrusion Detection on Network System, Data-driven Network Security, Anti-Virus Vendors, Botnet Defense Systems, Malicious Node Detection, Brain-Computer Interfaces Classifications, Cybersecurity Risks, Data-Driven Network Security, Deep Learning Techniques, Blockchain Systems, Linked LKH Algorithms, WSN Security, Machine Learning, Malicious Node Detection, Malware Algorithms, Malware Detection, SecBrain.

Specifically, this book contains discussion on the following topics:
- Machine Learning for Malware Analysis: Methods, Challenges, and Future Directions
- Research Trends for Malware and Intrusion Detection on Network System: A Topic Modelling Approach
- Deep-Learning and Machine-Learning-Based Techniques for Malware Detection and Data-Driven Network Security
- The Era of Advanced Machine Learning and Deep Learning Algorithms for Malware Detection
- Malware Detection in Industrial Scenarios Using Machine Learning and Deep Learning Techniques
- Malicious Node Detection using Convolution Technique: Authentication in Wireless Sensor Network (WSN)
- Scalable Rekeying Using Linked LKH Algorithm for Secure Multicast Communication
- Botnet Defense System and White-Hat Worm Launch Strategy in IoT Network
- A Survey on Emerging Security Issues, Challenges and Solutions for Internet of Things (IoTs)
- SecBrain: A Framework to Detect Cyberattacks Revealing Sensitive Data in Brain-Computer Interfaces
- A Study on Data Sharing using Blockchain System and Its Challenges and Applications
- Fruit Fly Optimization-Based Adversarial Modeling for Securing Wireless Sensor Network (WSN): Fruit Fly Optimization
- Cybersecurity Risks Associated With Brain-Computer Interfaces Classifications

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