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Название: Modern Vulnerability Management: Predictive Cybersecurity
Автор: Michael Roytman, Ed Bellis
Издательство: Artech House
Год: 2023
Страниц: 237
Язык: английский
Формат: pdf (true)
Размер: 13.9 MB
This book comprehensively covers the principles of Risk-based vulnerability management (RBVM) – one of the most challenging tasks in cybersecurity -- from the foundational mathematical models to building your own decision engine to identify, mitigate, and eventually forecast the vulnerabilities that pose the greatest threat to your organization. You will learn: how to structure data pipelines in security and derive and measure value from them; where to procure open-source data to better your organization’s pipeline and how to structure it; how to build a predictive model using vulnerability data; how to measure the return on investment a model in security can yield; which organizational structures and policies work best, and how to use Data Science to detect when they are not working in security; and ways to manage organizational change around Data Science implementation. Using the many sources of data we have and the mathematical modeling we use to compensate for mathematical scale, randomness, and uncertainty, Machine Learning (ML) offers a quick and efficient way to better prioritize decisions and remediate the vulnerabilities that pose the greatest risk. ML is a classification of Artificial Intelligence in which algorithms automatically improve over time by observing patterns in data and applying those patterns to subsequent actions.
Автор: Michael Roytman, Ed Bellis
Издательство: Artech House
Год: 2023
Страниц: 237
Язык: английский
Формат: pdf (true)
Размер: 13.9 MB
This book comprehensively covers the principles of Risk-based vulnerability management (RBVM) – one of the most challenging tasks in cybersecurity -- from the foundational mathematical models to building your own decision engine to identify, mitigate, and eventually forecast the vulnerabilities that pose the greatest threat to your organization. You will learn: how to structure data pipelines in security and derive and measure value from them; where to procure open-source data to better your organization’s pipeline and how to structure it; how to build a predictive model using vulnerability data; how to measure the return on investment a model in security can yield; which organizational structures and policies work best, and how to use Data Science to detect when they are not working in security; and ways to manage organizational change around Data Science implementation. Using the many sources of data we have and the mathematical modeling we use to compensate for mathematical scale, randomness, and uncertainty, Machine Learning (ML) offers a quick and efficient way to better prioritize decisions and remediate the vulnerabilities that pose the greatest risk. ML is a classification of Artificial Intelligence in which algorithms automatically improve over time by observing patterns in data and applying those patterns to subsequent actions.