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Predictive data security using AI : insights and issues of blockchain, IoT, and DevOps / Hiren Kumar Thakkar, Mayank Swarnkar, Robin Singh Bhadoria, editors.
Thakkar, Hiren Kumar, editor.; Swarnkar, Mayank, editor.; Bhadoria, Robin Singh, editor.
2023
QA76.9.A25
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Title
Predictive data security using AI : insights and issues of blockchain, IoT, and DevOps / Hiren Kumar Thakkar, Mayank Swarnkar, Robin Singh Bhadoria, editors.
ISBN
9789811962905 (electronic bk.)
9811962901 (electronic bk.)
9811962898
9789811962899
DOI
https://doi.org/10.1007/978-981-19-6290-5
Publication Details
Singapore : Springer, 2023.
Language
English
Description
1 online resource
Item Number
10.1007/978-981-19-6290-5 doi
Call Number
QA76.9.A25
Dewey Decimal Classification
005.8
Summary
This contributed volume consists of 11 chapters that specifically cover the security aspects of the latest technologies such as Blockchain, IoT, and DevOps, and how to effectively deal with them using Intelligent techniques. Moreover, machine learning (ML) and deep learning (DL) algorithms are also not secured and often manipulated by attackers for data stealing. This book also discusses the types of attacks and offers novel solutions to counter the attacks on ML and DL algorithms. This book describes the concepts and issues with figures and the supporting arguments with facts and charts. In addition to that, the book provides the comparison of different security solutions in terms of experimental results with tables and charts. Besides, the book also provides the future directions for each chapter and novel alternative approaches, wherever applicable. Often the existing literature provides domain-specific knowledge such as the description of security aspects. However, the readers find it difficult to understand how to tackle the application-specific security issues. This book takes one step forward and offers the security issues, current trends, and technologies supported by alternate solutions. Moreover, the book provides thorough guidance on the applicability of ML and DL algorithms to deal with application-specific security issues followed by novel approaches to counter threats to ML and DL algorithms. The book includes contributions from academicians, researchers, security experts, security architectures, and practitioners and provides an in-depth understanding of the mentioned issues.
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Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed December 8, 2022).
Added Author
Thakkar, Hiren Kumar, editor.
Swarnkar, Mayank, editor.
Bhadoria, Robin Singh, editor.
Series
Studies in computational intelligence ; v. 1065. 1860-9503
Available in Other Form
Print version: 9789811962899
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Record Appears in
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All Resources
Introduction to Data Security with Machine Learning: Traditional Methods vs Recent Trends
Data Security and Predictive Informatics: Issues, Challenges, and Opportunities
Data Security Analytics using Machine Learning: Supervised and Unsupervised Approaches
Data Security in Data Servers: Implementation of Security in Data Servers, Content Delivery Network Servers and Proxy Servers
Data Security in Multimedia using AI: Perspective and Practices
Data Security in Blockchain: Data Generation, Analysis and Predictions.

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