Big Data Analytics in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective

233 Pages·2023·10.778 MB·English

by  Govind P. Gupta (editor), Rakesh Tripathi (editor), Brij B. Gupta (editor), Kwok Tai Chui (editor)

Big Data Analytics in
Fog-Enabled IoT Networks
The integration of fog computing with the resource-limited Internet of Things
(IoT) network formulates the concept of the fog-enabled IoT system. Due to a
large number of IoT devices, the IoT is a main source of Big Data. A large vol-
ume of sensing data is generated by IoT systems such as smart cities and smart-
grid applications. A fundamental research issue is how to provide a fast and
efficient data analytics solution for fog-enabled IoT systems. Big Data Analytics
in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective focuses
on Big Data analytics in a fog-enabled-IoT system and provides a comprehen-
sive collection of chapters that touch on different issues related to healthcare
systems, cyber-threat detection, malware detection, and the security and pri-
vacy of IoT Big Data and IoT networks.
This book also emphasizes and facilitates a greater understanding of various
security and privacy approaches using advanced artificial intelligence and Big
Data technologies such as machine and deep learning, federated learning,
blockchain, and edge computing, as well as the countermeasures to overcome
the vulnerabilities of the fog-enabled IoT system.

Big Data Analytics in
Fog-Enabled IoT
Networks
Towards a Privacy and Security
Perspective
Edited By:
Govind P. Gupta, Rakesh Tripathi,
Brij B. Gupta, and Kwok Tai Chui
First edition published 2023
by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487–2742
and by CRC Press
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© 2023 selection and editorial matter, Govind P. Gupta, Rakesh Tripathi, Brij B. Gupta, and
Kwok Tai Chui; individual chapters, the contributors
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Library of Congress Cataloging-in-Publication Data
Names: Gupta, Govind P., 1979- editor. | Tripathi, Rakesh, editor. | Gupta, Brij,
1982- editor. | Chui, Kwok Tai, editor.
Title: Big data analytics in fog-enabled IoT networks : towards a privacy and security
perspective / edited by Govind P. Gupta, Rakesh Tripathi, Brij B. Gupta and Kwok Tai Chui.
Description: First edition. | Boca Raton : CRC Press, 2023. | Includes bibliographical
references.
Identifiers: LCCN 2022049027 (print) | LCCN 2022049028 (ebook) |
ISBN 9781032206448 (hardback) | ISBN 9781032206455 (paperback) |
ISBN 9781003264545 (ebook)
Subjects: LCSH: Internet of things--Security measures. | Cloud computing--Security
measures. | Big data. | Deep learning (Machine learning)
Classification: LCC TK5105.8857 .B54 2023 (print) | LCC TK5105.8857 (ebook) |
DDC 005.8--dc23/eng/20230103
LC record available at https://lccn.loc.gov/2022049027
LC ebook record available at https://lccn.loc.gov/2022049028]
ISBN: 9781032206448 (hbk)
ISBN: 9781032206455 (pbk)
ISBN: 9781003264545 (ebk)
DOI: 10.1201/9781003264545
Typeset in Adobe Caslon Pro
by KnowledgeWorks Global Ltd.
Contents
Preface vii
about the editors ix
contributors xiii
chaPter 1 deeP Learning techniques in
big data-enabLed internet-of-
things devices 1
SOURAV SINGH, SACHIN SHARMA, AND
SHUCHI BHADULA
chaPter 2 ioMt-based sMart heaLth Monitoring:
the future of heaLth care 35
INDRASHIS MITRA, YASHI SRIVASTAVA,
KANANBALA RAY, AND TEJASWINI KAR
chaPter 3 a review on intrusion
detection systeMs and
cyber threat inteLLigence for secure
iot-enabLed networks: chaLLenges and
directions 51
PRABHAT KUMAR, GOVIND P. GUPTA, AND
RAKESH TRIPATHI
v
vi Contents
chaPter 4 seLf-adaPtive aPPLication Monitoring for
decentraLized edge fraMeworks 77
MONIKA SAXENA, KIRTI PANDEY, VAIBHAV
VYAS, AND C.K. JHA
chaPter 5 federated Learning and its aPPLication in
MaLware detection 103
SAKSHI BHAGWAT AND GOVIND P. GUPTA
chaPter 6 an enseMbLe xGBoost aPProach for
the detection of cyber-attacks in the
industriaL iot doMain 125
R.K. PARERIYA, PRIYANKA VERMA, AND
PATHAN SUHANA
chaPter 7 a review on iot for the aPPLication
of energy, environMent, and waste
ManageMent: systeM architecture and
future directions 141
C. RAKESH, T. VIVEK, AND K. BALAJI
chaPter 8 anaLysis of feature seLection Methods
for android MaLware detection using
Machine Learning techniques 173
SANTOSH K. SMMARWAR, GOVIND P. GUPTA,
AND SANJAY KUMAR
chaPter 9 an efficient oPtiMizing energy
consuMPtion using Modified
bee coLony oPtiMization in
fog and iot networks 197
POTU NARAYANA, CHANDRASHEKAR JATOTH,
PREMCHAND PARAVATANENI, AND G. REKHA
Index 213
Preface
The rapid growth in the IoT technologies enables the large-scale
deployment and connectivity of smart IoT devices such as sensors,
smartphones, smart meters, smart vehicles, and so forth for differ-
ent applications such as smart cities, smart grids, smart homes, smart
healthcare systems, smart video surveillance, e-healthcare, etc. Due
to the limited resources of the IoT devices, both in terms of computa-
tion capabilities and energy resources, a fog computing infrastructure
is required to offload computation and storage from resource-limited
IoT devices to resource-rich fog nodes. Thus, the integration of fog
computing with the resource-limited IoT network formulates the
concept of a fog-enabled IoT system. Due to large number of deploy-
ments of IoT devices, the IoT is a main source of Big Data. A large
volume of sensing data are generated by IoT systems such as smart
cities and smart-grid applications. To provide a fast and efficient
data analytics solution for fog-enabled IoT systems is a fundamen-
tal research issue. For the deployment of the fog-enabled IoT system
in different applications such as healthcare systems, smart cities, and
smart-grid systems, the security and privacy of IoT Big Data and IoT
networks are key issues. The current centralized IoT architecture is
heavily restricted with various challenges such as a single point of fail-
ure, data privacy, security, robustness, etc. Thus, this book emphasizes
and facilitate a greater understanding of various security and privacy
vii
viii PrefaCe
approaches using advanced artificial intelligence and Big Data tech-
nologies like machine and deep learning, federated learning, block-
chain, edge computing as well as the countermeasures to overcome
these vulnerabilities.
Dr. Govind P. Gupta
Dr. Rakesh Tripathi
Dr. Brij B. Gupta
Dr. Kwok Tai Chui
About the Editors
Dr. Govind P. Gupta r eceived his Ph.D. in Computer Science &
Engineering from the Indian Institute of Technology, Roorkee, India.
He is currently working as an assistant professor in the Department
of Information Technology, National Institute of Technology, Raipur,
India. He has made practical and theoretical contributions in the Big
Data processing and analytics, Wireless Sensor Network, Internet
of Things (IoT), and cyber-security domains and his research has
a significant impact on Big Data analytics, WSN, IoT, informa-
tion, and network security against cyber-attacks. He has published
more than 50 research papers in reputed peer-reviewed journals
and conferences including IEEE, Elsevier, ACM, Springer, Wiley,
Inderscience, etc. He is an active reviewer of various journals such as
IEEE Internet of Things, IEEE Sensors Journal, IEEE Transactions on
Green Communications, Elsevier Computer Networks, Ad-Hoc Networks,
Computer Communications, Springer Wireless Networks, etc. His cur-
rent research interests include IoT, IoT security, software-defined
networking, network security, Big IoT Data analytics, blockchain-
based application development for IoT, and enterprise blockchain. He
is a professional member of the IEEE and ACM.
ix