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English
Academic Press Inc
23 October 2020
Security in IoT Social Networks takes a deep dive into security threats and risks, focusing on real-world social and financial effects. Mining and analyzing enormously vast networks is a vital part of exploiting Big Data. This book provides insight into the technological aspects of modeling, searching, and mining for corresponding research issues, as well as designing and analyzing models for resolving such challenges. The book will help start-ups grow, providing research directions concerning security mechanisms and protocols for social information networks. The book covers structural analysis of large social information networks, elucidating models and algorithms and their fundamental properties.

Moreover, this book includes smart solutions based on artificial intelligence, machine learning, and deep learning for enhancing the performance of social information network security protocols and models. This book is a detailed reference for academicians, professionals, and young researchers. The wide range of topics provides extensive information and data for future research challenges in present-day social information networks.

Edited by:   , , , , , , ,
Imprint:   Academic Press Inc
Country of Publication:   United States
Dimensions:   Height: 234mm,  Width: 191mm, 
Weight:   610g
ISBN:   9780128215999
ISBN 10:   0128215992
Series:   Intelligent Data-Centric Systems
Pages:   266
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
1. Security Issues and Technical Challenges in IoT-enabled Social Networks 2. Emerging Social IoT Applications and Security Assurance 3. Optimized Security Models and deep learning for Social IoT Networks 4. Key Agreement and Social IoT networks 5. Biometrics and Authentication Methods in the IoT Era 6. Forensic Analysis in Social IoT Applications 7. Recommended IoT Systems – Security Threats and Mechanisms 8. Enabling Cyber-Physical Technologies for Social IoT Networks 9. Deception Detection for Fake Injections in IoT Networks 10. Social Networking for medical and health care application

Dr. Fadi Al-Turjman is the Associate Dean for Research and the Founding Director of the International Research Center for AI and IoT at Near East University, Nicosia, Turkey. He received his Ph.D. in Computer Science from Queen’s University, Canada. Dr. Al-Turjman is a leading authority in the areas of smart/intelligent, wireless, and mobile networks. His publication history spans over 400 SCI/E publications, and more than 11,000 citations, in addition to numerous keynotes and plenary talks at flagship venues. He has authored and edited more than 40 books about cognition, security, and wireless sensor networks’ deployments in smart IoT environments, which have been published by well-reputed publishers such as Taylor and Francis, Elsevier, IET, and Springer. Dr. Al-Turjman received several recognitions and best papers’ awards at top international conferences, including Best Research Paper Award from Elsevier Computer Communications Journal. Dr. Al-Turjman has led a number of international symposia and workshops for flagship communication society conferences. Currently, he serves as book series editor and lead guest/associate editor for several top tier journals, including IEEE Communications Surveys and Tutorials and Elsevier Sustainable Cities and Society, in addition to organizing international conferences and symposiums on leading-edge research topics in AI and IoT. B.D. Deebak is an associate professor in the Department of Computational Intelligence, School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. Previously, he was at GMR Institute of Technology, Rajam as an associate professor in the Department of Computer Science and Engineering and before that in Middle East Technical University (METU) Northern Cyprus Campus. He has more than 12 years of teaching experience and research in various engineering institutions. His areas of research include multimedia networks, network security, and machine learning.

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