Predictive Data Security using AI (eBook)

Insights and Issues of Blockchain, IoT, and DevOps
Artikelnummer: 978-981-1962-90-5
Einband: PDF
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
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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.

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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VerlagSpringer Nature Singapore
EinbandPDF
Erscheinungsjahr2022
Seitenangabe216 S.
AusgabekennzeichenEnglisch
AbbildungenXII, 216 p. 90 illus., 68 illus. in color.
Masse4'662 KB
PlattformPDF
ReiheStudies in Computational Intelligence; Engineering; Engineering
AutorThakkar, Hiren Kumar (Hrsg.) / Swarnkar, Mayank (Hrsg.) / Bhadoria, Robin Singh (Hrsg.)

Alle Bände der Reihe "Studies in Computational Intelligence; Engineering; Engineering (R0)"

Über den Autor Hiren Kumar (Hrsg.) Thakkar

Dr. Hiren Kumar Thakkar received his M. Tech in CSE from IIIT Bhubaneswar, India in 2012 and a Ph.D. degree from Chang Gung University, Taiwan in 2018. Later, he worked as a postdoctoral fellow at Motor Behavioral Research Lab (MBRL), at Chang Gung University, Taiwan. Before joining PDEU, he worked as an Associate Professor at Nirma University, Ahmedabad. His research Interest includes Healthcare Data Analysis, Cloud Resource Management, and Opinion Feature Mining. He has published several SCI research papers in peer-reviewed journals and is a member of IEEE.Dr. Chintan Bhatt is an Assistant Professor in the Department of Computer Science and Engineering (CSE), School of Technology, Pandit Deendayal Energy University (PDEU). Before joining PDEU, Dr. Bhatt served as an Assistant Professor in the CE department (CSPIT, CHARUSAT) for 11 years. Dr. Bhatt is the author and co-author of 80+ publications in the areas of Computer Vision, the Internet of Things, and Fog Computing. He has won several awards including "CSI Award" and "Best Paper Award" for his CSI articles and conference publications.Dr. Victor C. M. Leung is an Emeritus Professor of Electrical and Computer Engineering at the University of British Columbia, Canada, and a Distinguished Professor of Computer Science and Software Engineering at Shenzhen University, China. He has co-authored numerous refereed articles in the broad areas of wireless networks and mobile systems and is a Clarivate Analytics "Highly Cited Researcher". Dr. received the 2017 IEEE ComSoc Fred W. Ellersick Prize, the 2017 IEEE Systems Journal Best Paper Award, the 2017 Canadian Award for Telecommunications Research, the 2018 IEEE TCGCC Distinguished Technical Achievement Recognition Award, the 2018 IEEE CSIM Best Journal Paper Award, and the 2019 IEEE TCGCC Best Journal Paper Award, as well as several other awards. He is a Life Fellow of IEEE, and a Fellow of the Royal Society of Canada, Canadian Academy of Engineering, and Engineering Institute of Canada.Dr. Ilangko Balasingham received the Siv. Ing.(MSc) and Dr. Ing.(Ph.D.) degrees both in signal processing from the Norwegian University of Science and Technology (NTNU), Trondheim, Norway in 1993 and 1998, respectively. He did his Masters thesis at the University of California in Santa Barbara. From 1998 to 2002, he was employed as a Research Engineer developing video streaming solutions for mobile handheld devices at Fast Search & Transfer ASA, Oslo, Norway. Since 2002, he has been with the Intervention Center, Oslo University Hospital - Rikshospitalet, Oslo, Norway as a Senior Research Scientist and Founder and Head of the Biomedical Sensor Network Research Group. He was appointed as a Professor of Medical Signal Processing and Communications at NTNU in 2006. In 2017, He was promoted to Head of the Section for Medical ICT R&D at the Intervention Center. He was a Professor by courtesy at the Frontier Institute, Nagoya Institute of Technology, Nagoya, Japan during the academic year 2016-2017. His research interests include medical signal and image processing, wireless biomedical sensor networks for short-range sensing, imaging, localization and communication, and multimedia patient record systems. Lately, he has been involved in nano-scale communication networks - molecular communications using biology and nanotechnology for intra-body applications, minimally invasive diagnosis, monitoring, and drug delivery.

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