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Data science in cybersecurity and cy...

Sikos, Leslie F.

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Data science in cybersecurity and cyberthreat intelligence

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science in cybersecurity and cyberthreat intelligence/ edited by Leslie F. Sikos, Kim-Kwang Raymond Choo.
other author:	Sikos, Leslie F.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xii, 129 p. :ill., digital ;24 cm.
[NT 15003449]:	The Formal Representation of Cyberthreats for Automated Reasoning -- A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks -- Discovering Malicious URLs Using Machine Learning Techniques -- Machine Learning and Big Data Processing for Cybersecurity Data Analysis -- Systematic Analysis of Security Implementation for Internet of Health Things in Mobile Health Networks -- Seven Pitfalls of Using Data Science in Cybersecurity.
Contained By:	Springer eBooks
Subject:	Computer security. -
Online resource:	https://doi.org/10.1007/978-3-030-38788-4
ISBN:	9783030387884

Data science in cybersecurity and cyberthreat intelligence
Data science in cybersecurity and cyberthreat intelligence[electronic resource] /edited by Leslie F. Sikos, Kim-Kwang Raymond Choo. - Cham :Springer International Publishing :2020. - xii, 129 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.1771868-4394 ;. - Intelligent systems reference library ;v.177..

The Formal Representation of Cyberthreats for Automated Reasoning -- A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks -- Discovering Malicious URLs Using Machine Learning Techniques -- Machine Learning and Big Data Processing for Cybersecurity Data Analysis -- Systematic Analysis of Security Implementation for Internet of Health Things in Mobile Health Networks -- Seven Pitfalls of Using Data Science in Cybersecurity.

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.

ISBN: 9783030387884

Standard No.: 10.1007/978-3-030-38788-4doiSubjects--Topical Terms:

540555
Computer security.

LC Class. No.: QA76.9.A25 / D383 2020

Dewey Class. No.: 005.8

Data science in cybersecurity and cyberthreat intelligence
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245 0 0 $a Data science in cybersecurity and cyberthreat intelligence $h [electronic resource] / $c edited by Leslie F. Sikos, Kim-Kwang Raymond Choo.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xii, 129 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.177
505 0 $a The Formal Representation of Cyberthreats for Automated Reasoning -- A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks -- Discovering Malicious URLs Using Machine Learning Techniques -- Machine Learning and Big Data Processing for Cybersecurity Data Analysis -- Systematic Analysis of Security Implementation for Internet of Health Things in Mobile Health Networks -- Seven Pitfalls of Using Data Science in Cybersecurity.
520 $a 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.
650 0 $a Computer security. $3 540555
650 1 4 $a Data Engineering. $3 3409361
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Cybercrime. $3 2195714
650 2 4 $a Computer Crime. $3 3382518
700 1 $a Sikos, Leslie F. $3 2115045
700 1 $a Choo, Kim-Kwang Raymond. $3 1001109
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Intelligent systems reference library ; $v v.177. $3 3448601
856 4 0 $u https://doi.org/10.1007/978-3-030-38788-4
950 $a Intelligent Technologies and Robotics (Springer-42732)