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Predictive data security using AIinsights and issues of blockchain, IoT, and DevOps /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predictive data security using AIedited by Hiren Kumar Thakkar, Mayank Swarnkar, Robin Singh Bhadoria.
其他題名:	insights and issues of blockchain, IoT, and DevOps /
其他作者:	Thakkar, Hiren Kumar.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xii, 216 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computer security.
電子資源:	https://doi.org/10.1007/978-981-19-6290-5
ISBN:	9789811962905$q(electronic bk.)

Predictive data security using AIinsights and issues of blockchain, IoT, and DevOps /
Predictive data security using AIinsights and issues of blockchain, IoT, and DevOps /[electronic resource] :edited by Hiren Kumar Thakkar, Mayank Swarnkar, Robin Singh Bhadoria. - Singapore :Springer Nature Singapore :2023. - xii, 216 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 10651860-9503 ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9789811962905$q(electronic bk.)

Standard No.: 10.1007/978-981-19-6290-5doiSubjects--Topical Terms:

184416
Computer security.

LC Class. No.: QA76.9.A25

Dewey Class. No.: 005.8

Predictive data security using AIinsights and issues of blockchain, IoT, and DevOps /
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520 $a 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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