Search

Browse Subject Areas

For Authors

Submit a Proposal

Convergence of Deep Learning in Cyber-IoT Systems and Security

Edited by Rajdeep Chakraborty, Anupam Ghosh, Jyotsna Kumar Mandal and S. Balamurugan
Series: Artificial Intelligence and Soft Computing for Industrial Transformation
Copyright: 2023   |   Status: Published
ISBN: 9781119857211  |  Hardcover  |  
468 pages | 222 illustrations
Price: $225 USD
Add To Cart

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

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

Description
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.

Back to Top
Author / Editor Details
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 is 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 is 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.

Back to Top

Table of Contents
Preface
Part I: Various Approaches from Machine Learning to Deep Learning
1. Web-Assisted Noninvasive Detection of Oral Submucous Fibrosis Using IoHT
Animesh Upadhyaya, Vertika Rai, Debdutta Pal, Surajit Bose and Somnath Ghosh
1.1 Introduction
1.2 Literature Survey
1.2.1 Oral Cancer
1.3 Primary Concepts
1.3.1 Transmission Efficiency
1.4 Propose Model
1.4.1 Platform Configuration
1.4.2 Harvard Architectural Microcontroller Base Wireless Communication Board
1.4.2.1 NodeMCU ESP8266 Microcontroller
1.4.2.2 Gas Sensor
1.4.3 Experimental Setup
1.4.4 Process to Connect to Sever and Analyzing Data on Cloud
1.5 Comparative Study
1.6 Conclusion
References
2. Performance Evaluation of Machine Learning and Deep Learning Techniques: A Comparative Analysis for House Price Prediction
Sajeev Ram Arumugam, Sheela Gowr, Abimala, Balakrishna and Oswalt Manoj
2.1 Introduction
2.2 Related Research
2.2.1 Literature Review on Comparing the Performance of the ML/DL Algorithms
2.2.2 Literature Review on House Price Prediction
2.3 Research Methodology
2.3.1 Data Collection
2.3.2 Data Visualization
2.3.3 Data Preparation
2.3.4 Regression Models
2.3.4.1 Simple Linear Regression
2.3.4.2 Random Forest Regression
2.3.4.3 Ada Boosting Regression
2.3.4.4 Gradient Boosting Regression
2.3.4.5 Support Vector Regression
2.3.4.6 Artificial Neural Network
2.3.4.7 Multioutput Regression
2.3.4.8 Regression Using Tensorflow—Keras
2.3.5 Classification Models
2.3.5.1 Logistic Regression Classifier
2.3.5.2 Decision Tree Classifier
2.3.5.3 Random Forest Classifier
2.3.5.4 Naïve Bayes Classifier
2.3.5.5 K-Nearest Neighbors Classifier
2.3.5.6 Support Vector Machine Classifier (SVM)
2.3.5.7 Feed Forward Neural Network
2.3.5.8 Recurrent Neural Networks
2.3.5.9 LSTM Recurrent Neural Networks
2.3.6 Performance Metrics for Regression Models
2.3.7 Performance Metrics for Classification Models
2.4 Experimentation
2.5 Results and Discussion
2.6 Suggestions
2.7 Conclusion
References
3. Cyber Physical Systems, Machine Learning & Deep Learning— Emergence as an Academic Program and Field for Developing Digital Society
P. K. Paul
3.1 Introduction
3.2 Objective of the Work
3.3 Methods
3.4 Cyber Physical Systems: Overview with Emerging Academic Potentiality
3.5 ML and DL Basics with Educational Potentialities
3.5.1 Machine Learning (ML)
3.5.2 Deep Learning
3.6 Manpower and Developing Scenario in Machine Learning and Deep Learning
3.7 DL & ML in Indian Context
3.8 Conclusion
References
4. Detection of Fake News and Rumors in the Social Media Using Machine Learning Techniques With Semantic Attributes
Diganta Saha, Arijit Das, Tanmay Chandra Nath, Soumyadip Saha and Ratul Das
4.1 Introduction
4.2 Literature Survey
4.3 Proposed Work
4.3.1 Algorithm
4.3.2 Flowchart
4.3.3 Explanation of Approach
4.4 Results and Analysis
4.4.1 Datasets
4.4.2 Evaluation
4.4.2.1 Result of 1st Dataset
4.4.2.2 Result of 2nd Dataset
4.4.2.3 Result of 3rd Dataset
4.4.3 Relative Comparison of Performance
4.5 Conclusion
References
Part II: Innovative Solutions Based on Deep Learning
5 Online Assessment System Using Natural Language Processing Techniques
S. Suriya, K. Nagalakshmi and Nivetha S.
5.1 Introduction
5.2 Literature Survey
5.3 Existing Algorithms
5.4 Proposed System Design
5.5 System Implementation
5.6 Conclusion
References
6. On a Reference Architecture to Build Deep-Q Learning-Based Intelligent IoT Edge Solutions
Amit Chakraborty, Ankit Kumar Shaw and Sucharita Samanta
6.1 Introduction
6.1.1 A Brief Primer on Machine Learning
6.1.1.1 Types of Machine Learning
6.2 Dynamic Programming
6.3 Deep Q-Learning
6.4 IoT
6.4.1 Azure
6.4.1.1 IoT on Azure
6.5 Conclusion
6.6 Future Work
References
7. Fuzzy Logic-Based Air Conditioner System
Suparna Biswas, Sayan Roy Chaudhuri, Ayusha Biswas and Arpan Bhawal
7.1 Introduction
7.2 Fuzzy Logic-Based Control System
7.3 Proposed System
7.3.1 Fuzzy Variables
7.3.2 Fuzzy Base Class
7.3.3 Fuzzy Rule Base
7.3.4 Fuzzy Rule Viewer
7.4 Simulated Result
7.5 Conclusion and Future Work
References
8. An Efficient Masked-Face Recognition Technique to Combat with COVID-19
Suparna Biswas
8.1 Introduction
8.2 Related Works
8.2.1 Review of Face Recognition for Unmasked Faces
8.2.2 Review of Face Recognition for Masked Faces
8.3 Mathematical Preliminaries
8.3.1 Digital Curvelet Transform (DCT)
8.3.2 Compressive Sensing–Based Classification
8.4 Proposed Method
8.5 Experimental Results
8.5.1 Database
8.5.2 Result
8.6 Conclusion
References
9. Deep Learning: An Approach to Encounter Pandemic Effect of Novel Corona Virus (COVID-19)
Santanu Koley, Pinaki Pratim Acharjya, Rajesh Mukherjee, Soumitra Roy and Somdeep Das
9.1 Introduction
9.2 Interpretation With Medical Imaging
9.3 Corona Virus Variants Tracing
9.4 Spreading Capability and Destructiveness of Virus
9.5 Deduction of Biological Protein Structure
9.6 Pandemic Model Structuring and Recommended Drugs
9.7 Selection of Medicine
9.8 Result Analysis
9.9 Conclusion
References
10. Question Answering System Using Deep Learning in the Low Resource Language Bengali
Arijit Das and Diganta Saha
10.1 Introduction
10.2 Related Work
10.3 Problem Statement
10.4 Proposed Approach
10.5 Algorithm
10.6 Results and Discussion
10.6.1 Result Summary for TDIL Dataset
10.6.2 Result Summary for SQuAD Dataset
10.6.3 Examples of Retrieved Answers
10.6.4 Calculation of TP, TN, FP, FN, Accuracy, Precision, Recall, and F1 score
10.6.5 Comparison of Result with other Methods and Dataset
10.7 Analysis of Error
10.8 Few Close Observations
10.9 Applications
10.10 Scope for Improvements
10.11 Conclusions
Acknowledgments
References
Part III: Security and Safety Aspects with Deep Learning
11. Secure Access to Smart Homes Using Biometric Authentication With RFID Reader for IoT Systems
K.S. Niraja and Sabbineni Srinivasa Rao
11.1 Introduction
11.2 Related Work
11.3 Framework for Smart Home Use Case With Biometric
11.3.1 RFID-Based Authentication and Its Drawbacks
11.4 Control Scheme for Secure Access (CSFSC)
11.4.1 Problem Definition
11.4.2 Biometric-Based RFID Reader Proposed Scheme
11.4.3 Reader-Based Procedures
11.4.4 Backend Server-Side Procedures
11.4.5 Reader Side Final Compute and Check Operations
11.5 Results Observed Based on Various Features With Proposed and Existing Methods
11.6 Conclusions and Future Work
References
12. MQTT-Based Implementation of Home Automation System Prototype With Integrated Cyber-IoT Infrastructure and Deep Learning–Based Security Issues
Arnab Chakraborty
12.1 Introduction
12.2 Architecture of Implemented Home Automation
12.3 Challenges in Home Automation
12.3.1 Distributed Denial of Service and Attack
12.3.2 Deep Learning–Based Solution Aspects
12.4 Implementation
12.4.1 Relay
12.4.2 DHT11
12.5 Results and Discussions
12.6 Conclusion
References
13. Malware Detection in Deep Learning
Sharmila Gaikwad and Jignesh Patil
13.1 Introduction to Malware
13.1.1 Computer Security
13.1.2 What Is Malware?
13.2 Machine Learning and Deep Learning for Malware Detection
13.2.1 Introduction to Machine Learning
13.2.2 Introduction to Deep Learning
13.2.3 Detection Techniques Using Deep Learning
13.3 Case Study on Malware Detection
13.3.1 Impact of Malware on Systems
13.3.2 Effect of Malware in a Pandemic Situation
13.4 Conclusion
References
14. Patron for Women: An Application for Womens Safety
Riya Sil, Snatam Kamila, Ayan Mondal, Sufal Paul, Santanu Sinha and Bishes Saha
14.1 Introduction
14.2 Background Study
14.3 Related Research
14.3.1 A Mobile-Based Women Safety Application (I safe App)
14.3.2 Lifecraft: An Android-Based Application System for Women Safety
14.3.3 Abhaya: An Android App for the Safety of Women
14.3.4 Sakhi—The Saviour: An Android Application to Help Women in Times of Social Insecurity
14.4 Proposed Methodology
14.4.1 Motivation and Objective
14.4.2 Proposed System
14.4.3 System Flowchart
14.4.4 Use-Case Model
14.4.5 Novelty of the Work
14.4.6 Comparison with Existing System
14.5 Results and Analysis
14.6 Conclusion and Future Work
References
15. Concepts and Techniques in Deep Learning Applications in the Field of IoT Systems and Security
Santanu Koley and Pinaki Pratim Acharjya
15.1 Introduction
15.2 Concepts of Deep Learning
15.3 Techniques of Deep Learning
15.3.1 Classic Neural Networks
15.3.1.1 Linear Function
15.3.1.2 Nonlinear Function
15.3.1.3 Sigmoid Curve
15.3.1.4 Rectified Linear Unit
15.3.2 Convolution Neural Networks
15.3.2.1 Convolution
15.3.2.2 Max-Pooling
15.3.2.3 Flattening
15.3.2.4 Full Connection
15.3.3 Recurrent Neural Networks
15.3.3.1 LSTMs
15.3.3.2 Gated RNNs
15.3.4 Generative Adversarial Networks
15.3.5 Self-Organizing Maps
15.3.6 Boltzmann Machines
15.3.7 Deep Reinforcement Learning
15.3.8 Auto Encoders
15.3.8.1 Sparse
15.3.8.2 Denoising
15.3.8.3 Contractive
15.3.8.4 Stacked
15.3.9 Back Propagation
15.3.10 Gradient Descent
15.4 Deep Learning Applications
15.4.1 Automatic Speech Recognition (ASR)
15.4.2 Image Recognition
15.4.3 Natural Language Processing
15.4.4 Drug Discovery and Toxicology
15.4.5 Customer Relationship Management
15.4.6 Recommendation Systems
15.4.7 Bioinformatics
15.5 Concepts of IoT Systems
15.6 Techniques of IoT Systems
15.6.1 Architecture
15.6.2 Programming Model
15.6.3 Scheduling Policy
15.6.4 Memory Footprint
15.6.5 Networking
15.6.6 Portability
15.6.7 Energy Efficiency
15.7 IoT Systems Applications
15.7.1 Smart Home
15.7.2 Wearables
15.7.3 Connected Cars
15.7.4 Industrial Internet
15.7.5 Smart Cities
15.7.6 IoT in Agriculture
15.7.7 Smart Retail
15.7.8 Energy Engagement
15.7.9 IoT in Healthcare
15.7.10 IoT in Poultry and Farming
15.8 Deep Learning Applications in the Field of IoT Systems
15.8.1 Organization of DL Applications for IoT in Healthcare
15.8.2 DeepSense as a Solution for Diverse IoT Applications
15.8.3 Deep IoT as a Solution for Energy Efficiency
15.9 Conclusion
References
16. Efficient Detection of Bioweapons for Agricultural Sector Using Narrowband Transmitter and Composite Sensing Architecture
Arghyadeep Nag, Labani Roy, Shruti, Soumen Santra and Arpan Deyasi
16.1 Introduction
16.2 Literature Review
16.3 Properties of Insects
16.4 Working Methodology
16.4.1 Sensing
16.4.1.1 Specific Characterization of a Particular Species
16.4.2 Alternative Way to Find Those Previously Sensing Parameters
16.4.3 Remedy to Overcome These Difficulties
16.4.4 Take Necessary Preventive Actions
16.5 Proposed Algorithm
16.6 Block Diagram and Used Sensors
16.6.1 Arduino Uno
16.6.2 Infrared Motion Sensor
16.6.3 Thermographic Camera
16.6.4 Relay Module
16.7 Result Analysis
16.8 Conclusion
References
17. A Deep Learning–Based Malware and Intrusion Detection Framework
Pavitra Kadiyala and Kakelli Anil Kumar
17.1 Introduction
17.2 Literature Survey
17.3 Overview of the Proposed Work
17.3.1 Problem Description
17.3.2 The Working Models
17.3.3 About the Dataset
17.3.4 About the Algorithms
17.4 Implementation
17.4.1 Libraries
17.4.2 Algorithm
17.5 Results 376
17.5.1 Neural Network Models
17.5.2 Accuracy
17.5.3 Web Frameworks
17.6 Conclusion and Future Work
References
18. Phishing URL Detection Based on Deep Learning Techniques
S. Carolin Jeeva and W. Regis Anne
18.1 Introduction
18.1.1 Phishing Life Cycle
18.1.1.1 Planning
18.1.1.2 Collection
18.1.1.3 Fraud
18.2 Literature Survey
18.3 Feature Generation
18.4 Convolutional Neural Network for Classification of Phishing vs Legitimate URLs
18.5 Results and Discussion
18.6 Conclusion
References
Web Citation
Part IV: Cyber Physical Systems
19. Cyber Physical System—The Gen Z
Jayanta Aich and Mst Rumana Sultana
19.1 Introduction
19.2 Architecture and Design
19.2.1 Cyber Family
19.2.2 Physical Family
19.2.3 Cyber-Physical Interface Family
19.3 Distribution and Reliability Management in CPS
19.3.1 CPS Components
19.3.2 CPS Models
19.4 Security Issues in CPS
19.4.1 Cyber Threats
19.4.2 Physical Threats
19.5 Role of Machine Learning in the Field of CPS
19.6 Application
19.7 Conclusion
References
20. An Overview of Cyber Physical System (CPS) Security, Threats, and Solutions
Krishna Keerthi Chennam, Fahmina Taranum and Maniza Hijab
20.1 Introduction
20.1.1 Motivation of Work
20.1.2 Organization of Sections
20.2 Characteristics of CPS
20.3 Types of CPS Security
20.4 Cyber Physical System Security Mechanism—Main Aspects
20.4.1 CPS Security Threats
20.4.2 Information Layer
20.4.3 Perceptual Layer
20.4.4 Application Threats
20.4.5 Infrastructure
20.5 Issues and How to Overcome Them
20.6 Discussion and Solutions
20.7 Conclusion
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page