Home » Convergence of Deep Learning in Cyber-IoT Systems and Security
Product Image

Convergence of Deep Learning in Cyber-IoT Systems and Security

€221.90
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
Age Group_Uncategorized
Age Group_Uncategorized
AI modeling
AI system
artificial neural network
authentication mechanism in IoT security
automatic-update
B01=Anupam Ghosh
B01=Jyotsna Kumar Mandal
B01=Rajdeep Chakraborty
B01=S. Balamurugan
binary analysis
binary codes
Category1=Non-Fiction
Category=UTD
Category=UYQ
Category=UYQM
cognitive cyber-physical system
convergence of deep learning
convolution neural networks
COP=United States
cyber security
cyber systems
cyber-IOT system and security
cyber-IoT systems
cyber-physical system
cyber-physical system and security
cyber-physical systems
cybercrime
cyberspace
deep and restricted Boltzmann machines
deep belief networks
Deep learning
deep reinforcement learning
Delivery_Delivery within 10-20 working days
domain name generation algorithms
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
internet traffic
intrusion detection
IoT grid
IoT security
IoT systems
IP-spoofing
Language_English
long short- term memory
machine learning
malware detection
PA=Not available (reason unspecified)
penetration testing
phishing attack
Price_€100 and above
protocols for IoT security
PS=Active
recurrent neural networks
security analysis tool
security protocol
smart system
softlaunch
spam detection
static analysis
traffic analysis
trusted systems
unconventional cryptographic methods
vulnerability

Product details

  • ISBN 9781119857211
  • Weight: 907g
  • Publication Date: 09 Dec 2022
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
Secure checkout Fast Shipping Easy returns
Description
CONVERGENCE OF DEEP LEARNING IN CYBER-IOT SYSTEMS AND SECURITY

In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years.

The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems.

This book provides innovative solutions and implementation of deep learning-based models in cyber-IoT systems, as well as the exposed security issues in these systems. The 20 chapters are organized into four parts. Part I gives the various approaches that have evolved from machine learning to deep learning. Part II presents many innovative solutions, algorithms, models, and implementations based on deep learning. Part III covers security and safety aspects with deep learning. Part IV details cyber-physical systems as well as a discussion on the security and threats in cyber-physical systems with probable solutions.

Audience

Researchers and industry engineers in computer science, information technology, electronics and communication, cybersecurity and cryptography.
About

Rajdeep Chakraborty, PhD, is an assistant professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India. His fields of interest are mainly in cryptography and computer security. He was awarded the Adarsh Vidya Saraswati Rashtriya Puraskar, National Award of Excellence 2019 conferred by Glacier Journal Research Foundation,

Anupam Ghosh, PhD, is a professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India. He has published more than 80 international papers in reputed international journals and conferences. His fields of interest are mainly in AI, machine learning, deep learning, image processing, soft computing, bioinformatics, IoT, and data mining.

Jyotsna Kumar Mandal, PhD, has more than 30 years of industry and academic experience. His fields of interest are coding theory, data and network security, remote sensing & GIS-based applications, data compression error corrections, information security, watermarking, steganography and document authentication, image processing, visual cryptography, MANET, wireless and mobile computing/security, unify computing, chaos theory, and applications.

S. Balamurugan, PhD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.