£126.00

Springer Data Science in Cybersecurity and Cyberthreat Intelligence

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Description

Protect modern digital infrastructures with this advanced technical resource from Springer. This book provides a comprehensive look at utilizing machine learning, formal knowledge bases, and semantic reasoning to defend against evolving digital threats. It addresses the growing need for automated security measures in complex environments like IoT infrastructures and enterprise networks. Readers will explore state-of-the-art approaches to detecting attacks on communication networks and automating the detection of malicious code. The text covers essential security topics including the identification of malicious URLs and DGA-generated domain names. It also provides insights into improving the security of mHealth wearables and predicting cyberattacks within enterprise settings. By analyzing the likelihood of vulnerability exploitation through machine learning classifiers, this volume offers a modern alternative to traditional security methods. It is an essential guide for professionals and researchers looking to implement data science techniques to strengthen cyberthreat intelligence and network defense.

Key Features

Learn to use machine learning and semantic reasoning to detect attacks on communication networks and IoT infrastructures.

Explore methods to automate malicious code detection and improve the security of mHealth wearables.

Gain insights into predicting cyberattacks within enterprise environments to enhance proactive defense.

Study techniques for identifying malicious URLs and DGA-generated domain names to secure web traffic.

Discover how machine learning classifiers offer alternatives to traditional vulnerability exploitation analysis.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
06 February 2020
Listed Since
04 December 2019

Barcode

No barcode data available

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