Advances in Malware and Data-Driven Network Security

Brij B. Gupta

ISBN 10: 1799877906 / ISBN 13: 9781799877905

Editore: Information Science Reference, 2021

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Codice articolo 9781799877905

Dati bibliografici

• Titolo: Advances in Malware and Data-Driven Network Security
• Editore: Information Science Reference
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1799877906
• ISBN 13: 9781799877905
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 280 L x 216 A x 18 P (millimetri) 280 millimetri di larghezza per 216 millimetri di altezza per 18 millimetri di profondità
• Peso: 838 grammi

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Riassunto

"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"--