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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.
Inhaltsverzeichnis
Preface xvii
Part I: Various Approaches from Machine Learning to Deep Learning 1
1 Web-Assisted Noninvasive Detection of Oral Submucous Fibrosis Using IoHT 3
Animesh Upadhyaya, Vertika Rai, Debdutta Pal, Surajit Bose and Somnath Ghosh
1.1 Introduction 3
1.2 Literature Survey 6
1.2.1 Oral Cancer 6
1.3 Primary Concepts 7
1.3.1 Transmission Efficiency 7
1.4 Propose Model 9
1.4.1 Platform Configuration 9
1.4.2 Harvard Architectural Microcontroller Base Wireless Communication Board 10
1.4.2.1 NodeMCU ESP8266 Microcontroller 10
1.4.2.2 Gas Sensor 12
1.4.3 Experimental Setup 13
1.4.4 Process to Connect to Sever and Analyzing Data on Cloud 14
1.5 Comparative Study 16
1.6 Conclusion 17
References 17
2 Performance Evaluation of Machine Learning and Deep Learning Techniques: A Comparative Analysis for House Price Prediction 21
Sajeev Ram Arumugam, Sheela Gowr, Abimala, Balakrishna and Oswalt Manoj
2.1 Introduction 22
2.2 Related Research 23
2.2.1 Literature Review on Comparing the Performance of the ML/DL Algorithms 23
2.2.2 Literature Review on House Price Prediction 25
2.3 Research Methodology 26
2.3.1 Data Collection 27
2.3.2 Data Visualization 27
2.3.3 Data Preparation 28
2.3.4 Regression Models 29
2.3.4.1 Simple Linear Regression 29
2.3.4.2 Random Forest Regression 30
2.3.4.3 Ada Boosting Regression 31
2.3.4.4 Gradient Boosting Regression 32
2.3.4.5 Support Vector Regression 33
2.3.4.6 Artificial Neural Network 34
2.3.4.7 Multioutput Regression 36
2.3.4.8 Regression Using Tensorflow--Keras 37
2.3.5 Classification Models 39
2.3.5.1 Logistic Regression Classifier 39
2.3.5.2 Decision Tree Classifier 39
2.3.5.3 Random Forest Classifier 41
2.3.5.4 Naïve Bayes Classifier 41
2.3.5.5 K-Nearest Neighbors Classifier 42
2.3.5.6 Support Vector Machine Classifier (SVM) 43
2.3.5.7 Feed Forward Neural Network 43
2.3.5.8 Recurrent Neural Networks 44
2.3.5.9 LSTM Recurrent Neural Networks 44
2.3.6 Performance Metrics for Regression Models 45
2.3.7 Performance Metrics for Classification Models 46
2.4 Experimentation 47
2.5 Results and Discussion 48
2.6 Suggestions 60
2.7 Conclusion 60
References 62
3 Cyber Physical Systems, Machine Learning & Deep Learning-- Emergence as an Academic Program and Field for Developing Digital Society 67
P. K. Paul
3.1 Introduction 68
3.2 Objective of the Work 69
3.3 Methods 69
3.4 Cyber Physical Systems: Overview with Emerging Academic Potentiality 70
3.5 ml and dl Basics with Educational Potentialities 72
3.5.1 Machine Learning (ML) 72
3.5.2 Deep Learning 73
3.6 Manpower and Developing Scenario in Machine Learning and Deep Learning 74
3.7 dl & ml in Indian Context 79
3.8 Conclusion 81
References 82
4 Detection of Fake News and Rumors in the Social Media Using Machine Learning Techniques With Semantic Attributes 85
Diganta Saha, Arijit Das, Tanmay Chandra Nath, Soumyadip Saha and Ratul Das
4.1 Introduction 86
4.2 Literature Survey 87
4.3 Proposed Work 88
4.3.1 Algorithm 89
4.3.2 Flowchart 90
4.3.3 Explanation of Approach 91
4.4 Results and Analysis 92
4.4.1 Datasets 92
Über den Autor / die Autorin
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.
Zusammenfassung
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.
