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Data science in cybersecurity and cy...

Sikos, Leslie F.

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Data science in cybersecurity and cyberthreat intelligence

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science in cybersecurity and cyberthreat intelligence/ edited by Leslie F. Sikos, Kim-Kwang Raymond Choo.
其他作者:	Sikos, Leslie F.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xii, 129 p. :ill., digital ;24 cm.
內容註:	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
標題:	Computer security. -
電子資源:	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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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.
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