Machine Learning for Cyber Security / / ed. by Preeti Malik, Lata Nautiyal, Mangey Ram.

This book shows how machine learning (ML) methods can be used to enhance cyber security operations, including detection, modeling, monitoring as well as defense against threats to sensitive data and security systems. Filling an important gap between ML and cyber security communities, it discusses to...

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Bibliographic Details
Superior document:Title is part of eBook package: De Gruyter DG Plus DeG Package 2023 Part 1
MitwirkendeR:
HerausgeberIn:
Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2022]
©2023
Year of Publication:2022
Language:English
Series:De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences , 15
Online Access:
Physical Description:1 online resource (X, 148 p.)
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Description
Other title:Frontmatter --
Preface --
Contents --
List of contributors --
Editor’s biography --
Differential privacy: a solution to privacy issue in social networks --
Cracking Captcha using machine learning algorithms: an intersection of Captcha categories and ML algorithms --
The ransomware: an emerging security challenge to the cyberspace --
Property-based attestation in device swarms: a machine learning approach --
A review of machine learning techniques in cybersecurity and research opportunities --
A framework for seborrheic keratosis skin disease identification using Vision Transformer --
Mapping AICTE cybersecurity curriculum onto CyBOK: a case study --
Index
Summary:This book shows how machine learning (ML) methods can be used to enhance cyber security operations, including detection, modeling, monitoring as well as defense against threats to sensitive data and security systems. Filling an important gap between ML and cyber security communities, it discusses topics covering a wide range of modern and practical ML techniques, frameworks and tools.
Format:Mode of access: Internet via World Wide Web.
ISBN:9783110766745
9783111175782
9783110993899
9783110994810
9783110994223
9783110994193
ISSN:2626-5427 ;
DOI:10.1515/9783110766745
Access:restricted access
Hierarchical level:Monograph
Statement of Responsibility: ed. by Preeti Malik, Lata Nautiyal, Mangey Ram.