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

• Superior document: Title is part of eBook package: De Gruyter DG Plus DeG Package 2023 Part 1
• MitwirkendeR: Aswal, Kiran,; Baliyan, Nikhil,; Dobhal, Dinesh C.,; Goyal, Nupur,; Kamika,,; Kumar Roy, Vikas,; Kumar Singh, Ashwini,; Malik, Preeti,; Mittal, Mohit,; Mittal, Sangeeta,; Mittal, Varsha,; Nanath, Krishnadas,; Nautiyal, Rohit,; Nijhawan, Rahul,; Pathak, Heman,; Rahman, Abdul,; Rawat, Swati,; Thakur, Vasu,; Tiwari, Umesh K.,; Wedaj Kibret, Samuel,
• HerausgeberIn: Malik, Preeti,; Nautiyal, Lata,; Ram, Mangey,
• 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
• Physical Description: 1 online resource (X, 148 p.)

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.

Table of Contents

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