Data Science in Cybersecurity and Cyberthreat Intelligence (Paperback, 1st ed. 2020)

This book presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details how analyzing the likelihood of vulnerability exploitation using machine learning classifiers can offer an alternative to traditional penetration testing solutions. In addition, the book describes a range of techniques that support data aggregation and data fusion to automate data-driven analytics in cyberthreat intelligence, allowing complex and previously unknown cyberthreats to be identified and classified, and countermeasures to be incorporated in novel incident response and intrusion detection mechanisms.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Intelligent Systems Reference Library, 177
Release date:	February 2021
First published:	2020
Editors:	Leslie F Sikos • Kim-Kwang Raymond Choo
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	129
Edition:	1st ed. 2020
ISBN-13:	978-3-03-038790-7
Categories:	Books > Computing & IT > Computer communications & networking > Network security Books > Computing & IT > Social & legal aspects of computing > Computer fraud & hacking Books > Social sciences > Sociology, social studies > Crime & criminology > General Books > Law > Laws of other jurisdictions & general law > Financial, taxation, commercial, industrial law > Communications law Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-038790-9
Barcode:	9783030387907

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