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Advances in Malware and Data-Driven Network Security

Advances in Malware and Data-Driven Network Security

Brij B. Gupta
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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.
年:
2022
出版社:
Information Science Reference
语言:
english
页:
332
ISBN 10:
1799877914
ISBN 13:
9781799877912
系列:
Advances in Information Security, Privacy, and Ethics
文件:
PDF, 12.56 MB
IPFS:
CID , CID Blake2b
english, 2022
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