Convergence of Deep Learning in Cyber-IoT Systems and Security
Description
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.
More details
Other editions
Additional editions
Persons
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.
Content
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
4.4.2 Evaluation 93
4.4.2.1 Result of 1st Dataset 93
4.4.2.2 Result of 2nd Dataset 94
4.4.2.3 Result of 3rd Dataset 94
4.4.3 Relative Comparison of Performance 95
4.5 Conclusion 95
References 96
Part II: Innovative Solutions Based on Deep Learning 99
5 Online Assessment System Using Natural Language Processing Techniques 101
S. Suriya, K. Nagalakshmi and Nivetha S.
5.1 Introduction 102
5.2 Literature Survey 103
5.3 Existing Algorithms 108
5.4 Proposed System Design 111
5.5 System Implementation 115
5.6 Conclusion 120
References 121
6 On a Reference Architecture to Build Deep-Q Learning-Based Intelligent IoT Edge Solutions 123
Amit Chakraborty, Ankit Kumar Shaw and Sucharita Samanta
6.1 Introduction 124
6.1.1 A Brief Primer on Machine Learning 124
6.1.1.1 Types of Machine Learning 124
6.2 Dynamic Programming 128
6.3 Deep Q-Learning 129
6.4 IoT 130
6.4.1 Azure 130
6.4.1.1 IoT on Azure 130
6.5 Conclusion 144
6.6 Future Work 144
References 145
7 Fuzzy Logic-Based Air Conditioner System 147
Suparna Biswas, Sayan Roy Chaudhuri, Ayusha Biswas and Arpan Bhawal
7.1 Introduction 147
7.2 Fuzzy Logic-Based Control System 149
7.3 Proposed System 149
7.3.1 Fuzzy Variables 149
7.3.2 Fuzzy Base Class 154
7.3.3 Fuzzy Rule Base 155
7.3.4 Fuzzy Rule Viewer 156
7.4 Simulated Result 157
7.5 Conclusion and Future Work 163
References 163
8 An Efficient Masked-Face Recognition Technique to Combat with COVID- 19 165
Suparna Biswas
8.1 Introduction 165
8.2 Related Works 167
8.2.1 Review of Face Recognition for Unmasked Faces 167
8.2.2 Review of Face Recognition for Masked Faces 168
8.3 Mathematical Preliminaries 169
8.3.1 Digital Curvelet Transform (DCT) 169
8.3.2 Compressive Sensing-Based Classification 170
8.4 Proposed Method 171
8.5 Experimental Results 173
8.5.1 Database 173
8.5.2 Result 175
8.6 Conclusion 179
References 179
9 Deep Learning: An Approach to Encounter Pandemic Effect of Novel Corona Virus (COVID-19) 183
Santanu Koley, Pinaki Pratim Acharjya, Rajesh Mukherjee, Soumitra Roy and Somdeep Das
9.1 Introduction 184
9.2 Interpretation With Medical Imaging 185
9.3 Corona Virus Variants Tracing 188
9.4 Spreading Capability and Destructiveness of Virus 191
9.5 Deduction of Biological Protein Structure 192
9.6 Pandemic Model Structuring and Recommended Drugs 192
9.7 Selection of Medicine 195
9.8 Result Analysis 197
9.9 Conclusion 201
References 202
10 Question Answering System Using Deep Learning in the Low Resource Language Bengali 207
Arijit Das and Diganta Saha
10.1 Introduction 208
10.2 Related Work 210
10.3 Problem Statement 215
10.4 Proposed Approach 215
10.5 Algorithm 216
10.6 Results and Discussion 219
10.6.1 Result Summary for TDIL Dataset 219
10.6.2 Result Summary for SQuAD Dataset 219
10.6.3 Examples of Retrieved Answers 220
10.6.4 Calculation of TP, TN, FP, FN, Accuracy, Precision, Recall, and F1 score 221
10.6.5 Comparison of Result with other Methods and Dataset 222
10.7 Analysis of Error 223
10.8 Few Close Observations 223
10.9 Applications 224
10.10 Scope for Improvements 224
10.11 Conclusions 224
Acknowledgments 225
References 225
Part III: Security and Safety Aspects with Deep Learning 231
11 Secure Access to Smart Homes Using Biometric Authentication With RFID Reader for IoT Systems 233
K.S. Niraja and Sabbineni Srinivasa Rao
11.1 Introduction 234
11.2 Related Work 235
11.3 Framework for Smart Home Use Case With Biometric 236
11.3.1 RFID-Based Authentication and Its Drawbacks 236
11.4 Control Scheme for Secure Access (CSFSC) 237
11.4.1 Problem Definition 237
11.4.2 Biometric-Based RFID Reader Proposed Scheme 238
11.4.3 Reader-Based Procedures 240
11.4.4 Backend Server-Side Procedures 240
11.4.5 Reader Side Final Compute and Check Operations 240
11.5 Results Observed Based on Various Features With Proposed and Existing Methods 242
11.6 Conclusions and Future Work 245
References 246
12 MQTT-Based Implementation of Home Automation System Prototype With Integrated Cyber-IoT Infrastructure and Deep Learning-Based Security Issues 249
Arnab Chakraborty
12.1 Introduction 250
12.2 Architecture of Implemented Home Automation 252
12.3 Challenges in Home Automation 253
12.3.1 Distributed Denial of Service and Attack 254
12.3.2 Deep Learning-Based Solution Aspects 254
12.4 Implementation 255
12.4.1 Relay 256
12.4.2 DHT 11 257
12.5 Results and Discussions 262
12.6 Conclusion 265
References 266
13 Malware Detection in Deep Learning 269
Sharmila Gaikwad and Jignesh Patil
13.1 Introduction to Malware 270
13.1.1 Computer Security 270
13.1.2 What Is Malware? 271
13.2 Machine Learning and Deep Learning for Malware Detection 274
13.2.1 Introduction to Machine Learning 274
13.2.2 Introduction to Deep Learning 276
13.2.3 Detection Techniques Using Deep Learning 279
13.3 Case Study on Malware Detection 280
13.3.1 Impact of Malware on Systems 280
13.3.2 Effect of Malware in a Pandemic Situation 281
13.4 Conclusion 283
References 283
14 Patron for Women: An Application for Womens Safety 285
Riya Sil, Snatam Kamila, Ayan Mondal, Sufal Paul, Santanu Sinha and Bishes Saha
14.1 Introduction 286
14.2 Background Study 286
14.3 Related Research 287
14.3.1 A Mobile-Based Women Safety Application (I safe App) 287
14.3.2 Lifecraft: An Android-Based Application System for Women Safety 288
14.3.3 Abhaya: An Android App for the Safety of Women 288
14.3.4 Sakhi-The Saviour: An Android Application to Help Women in Times of Social Insecurity 289
14.4 Proposed Methodology 289
14.4.1 Motivation and Objective 290
14.4.2 Proposed System 290
14.4.3 System Flowchart 291
14.4.4 Use-Case Model 291
14.4.5 Novelty of the Work 294
14.4.6 Comparison with Existing System 294
14.5 Results and Analysis 294
14.6 Conclusion and Future Work 298
References 299
15 Concepts and Techniques in Deep Learning Applications in the Field of IoT Systems and Security 303
Santanu Koley and Pinaki Pratim Acharjya
15.1 Introduction 304
15.2 Concepts of Deep Learning 307
15.3 Techniques of Deep Learning 308
15.3.1 Classic Neural Networks 309
15.3.1.1 Linear Function 309
15.3.1.2 Nonlinear Function 309
15.3.1.3 Sigmoid Curve 310
15.3.1.4 Rectified Linear Unit 310
15.3.2 Convolution Neural Networks 310
15.3.2.1 Convolution 311
15.3.2.2 Max-Pooling 311
15.3.2.3 Flattening 311
15.3.2.4 Full Connection 311
15.3.3 Recurrent Neural Networks 312
15.3.3.1 LSTMs 312
15.3.3.2 Gated RNNs 312
15.3.4 Generative Adversarial Networks 313
15.3.5 Self-Organizing Maps 314
15.3.6 Boltzmann Machines 315
15.3.7 Deep Reinforcement Learning 315
15.3.8 Auto Encoders 316
15.3.8.1 Sparse 317
15.3.8.2 Denoising 317
15.3.8.3 Contractive 317
15.3.8.4 Stacked 317
15.3.9 Back Propagation 317
15.3.10 Gradient Descent 318
15.4 Deep Learning Applications 319
15.4.1 Automatic Speech Recognition (ASR) 319
15.4.2 Image Recognition 320
15.4.3 Natural Language Processing 320
15.4.4 Drug Discovery and Toxicology 321
15.4.5 Customer Relationship Management 322
15.4.6 Recommendation Systems 323
15.4.7 Bioinformatics 324
15.5 Concepts of IoT Systems 325
15.6 Techniques of IoT Systems 326
15.6.1 Architecture 326
15.6.2 Programming Model 327
15.6.3 Scheduling Policy 329
15.6.4 Memory Footprint 329
15.6.5 Networking 332
15.6.6 Portability 332
15.6.7 Energy Efficiency 333
15.7 IoT Systems Applications 333
15.7.1 Smart Home 334
15.7.2 Wearables 335
15.7.3 Connected Cars 335
15.7.4 Industrial Internet 336
15.7.5 Smart Cities 337
15.7.6 IoT in Agriculture 337
15.7.7 Smart Retail 338
15.7.8 Energy Engagement 339
15.7.9 IoT in Healthcare 340
15.7.10 IoT in Poultry and Farming 340
15.8 Deep Learning Applications in the Field of IoT Systems 341
15.8.1 Organization of DL Applications for IoT in Healthcare 342
15.8.2 DeepSense as a Solution for Diverse IoT Applications 343
15.8.3 Deep IoT as a Solution for Energy Efficiency 346
15.9 Conclusion 346
References 347
16 Efficient Detection of Bioweapons for Agricultural Sector Using Narrowband Transmitter and Composite Sensing Architecture 349
Arghyadeep Nag, Labani Roy, Shruti, Soumen Santra and Arpan Deyasi
16.1 Introduction 350
16.2 Literature Review 353
16.3 Properties of Insects 355
16.4 Working Methodology 357
16.4.1 Sensing 357
16.4.1.1 Specific Characterization of a Particular Species 357
16.4.2 Alternative Way to Find Those Previously Sensing Parameters 357
16.4.3 Remedy to Overcome These Difficulties 358
16.4.4 Take Necessary Preventive Actions 358
16.5 Proposed Algorithm 359
16.6 Block Diagram and Used Sensors 360
16.6.1 Arduino Uno 361
16.6.2 Infrared Motion Sensor 362
16.6.3 Thermographic Camera 362
16.6.4 Relay Module 362
16.7 Result Analysis 362
16.8 Conclusion 363
References 363
17 A Deep Learning-Based Malware and Intrusion Detection Framework 367
Pavitra Kadiyala and Kakelli Anil Kumar
17.1 Introduction 367
17.2 Literature Survey 368
17.3 Overview of the Proposed Work 371
17.3.1 Problem Description 371
17.3.2 The Working Models 371
17.3.3 About the Dataset 371
17.3.4 About the Algorithms 373
17.4 Implementation 374
17.4.1 Libraries 374
17.4.2 Algorithm 376
17.5 Results 376
17.5.1 Neural Network Models 377
17.5.2 Accuracy 377
17.5.3 Web Frameworks 377
17.6 Conclusion and Future Work 379
References 380
18 Phishing URL Detection Based on Deep Learning Techniques 381
S. Carolin Jeeva and W. Regis Anne
18.1 Introduction 382
18.1.1 Phishing Life Cycle 382
18.1.1.1 Planning 383
18.1.1.2 Collection 384
18.1.1.3 Fraud 384
18.2 Literature Survey 385
18.3 Feature Generation 388
18.4 Convolutional Neural Network for Classification of Phishing vs Legitimate URLs 388
18.5 Results and Discussion 391
18.6 Conclusion 394
References 394
Web Citation 396
Part IV: Cyber Physical Systems 397
19 Cyber Physical System-The Gen Z 399
Jayanta Aich and Mst Rumana Sultana
19.1 Introduction 399
19.2 Architecture and Design 400
19.2.1 Cyber Family 401
19.2.2 Physical Family 401
19.2.3 Cyber-Physical Interface Family 402
19.3 Distribution and Reliability Management in CPS 403
19.3.1 CPS Components 403
19.3.2 CPS Models 404
19.4 Security Issues in CPS 405
19.4.1 Cyber Threats 405
19.4.2 Physical Threats 407
19.5 Role of Machine Learning in the Field of CPS 408
19.6 Application 411
19.7 Conclusion 411
References 411
20 An Overview of Cyber Physical System (CPS) Security, Threats, and Solutions 415
Krishna Keerthi Chennam, Fahmina Taranum and Maniza Hijab
20.1 Introduction 416
20.1.1 Motivation of Work 417
20.1.2 Organization of Sections 417
20.2 Characteristics of CPS 418
20.3 Types of CPS Security 419
20.4 Cyber Physical System Security Mechanism-Main Aspects 421
20.4.1 CPS Security Threats 423
20.4.2 Information Layer 423
20.4.3 Perceptual Layer 424
20.4.4 Application Threats 424
20.4.5 Infrastructure 425
20.5 Issues and How to Overcome Them 426
20.6 Discussion and Solutions 427
20.7 Conclusion 431
References 431
Index 435
Preface
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analogue. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Early work showed that a linear perceptron cannot be a universal classifier, but that a network with a nonpolynomial activation function with one hidden layer of unbounded width can. Deep learning is a modern variation which is concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation, while retaining theoretical universality under mild conditions. In deep learning the layers are also permitted to be heterogeneous and to deviate widely from biologically informed connectionist models, for the sake of efficiency, trainability and understandability, whence the "structured" part.
Deep learning approaches are now used in every aspect of cyber systems and IoT systems. 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.
Thus, the book covers the evolution of deep learning from machine learning in the very first section. It also provides innovative projects and implementation on deep learning-based solutions in Cyber-IoT systems as well as detailed aspect on deep learning-based security issues in Cyber-IoT systems. Finally, this book also covers cyber physical system concepts and security issues. Therefore, we feel the book is well suited for students (UG & PG), research scholars, and enthusiastic readers who wants a good domain knowledge on deep learning.
Part I: Various Approaches from Machine Learning to Deep Learning
Chapter 1 discuss a method using web-assisted non-invasive detection of oral submucous fibrosis using IoHT. Oral cancer is another big threat and disease of human race. The early detection of oral cancer is of the upmost importance. This chapter starts with a detailed literature on oral cancer then it gives all the primary concepts to understand the proposed system of the authors. Then the chapter gives a proposed model to detect noninvasive oral submucous fibrosis which is web-assisted and for IoHT.
Chapter 2 provides a performance evaluation of machine learning and deep learning, the case study used in this chapter is house price prediction. After the introduction, the chapter starts with a detailed literature review of machine learning and deep learning. The authors used a very standard research methodology to carry out this study, which is discussed next. Authors used Gradient Boosting Regression, Support Vector Regression (SVM), Support Vector Regression (SVM), Multi Output Regression, Regression using Tensorflow - Keras and various classification models for their study. The authors concluded with very good performance analysis and results in both tabular and graphical forms.
Chapter 3 gives why it is important to study cyber physical systems, machine learning & deep learning. First, the author discusses cyber physical systems, machine learning & deep learning in detail. Then the author gives a detailed academic program in cyber physical systems, machine learning & deep learning throughout the world and in India. Finally, the author concludes with the importance to study cyber physical systems, machine learning & deep learning.
Chapter 4 proposes a hybrid model using machine learning techniques and semantic attribute to detect fake news, which is one of the important applications in this cyber and social media world. It uses NLP and applied to various datasets from Facebook, Twitter, Whatsapp, etc. The results are finally compared with existing literature and the proposed model is found to be 93% accurate.
Part II: Innovative Solutions based on Deep learning
Chapter 5 gives an online assessment system using Natural Language Processing (NLP) techniques. It starts with the importance of online assessment in the 'home from work' scenario due to the COVID-19 pandemic. The chapter then moves to a detailed literature on online assessments. Thereafter, the authors discuss some algorithms for online assessments. In the next section it proposes a system design for online assessment. Finally, implementation is shown and concluded with it's novelty.
Chapter 6 gives a reference architecture to build deep Q learning-based intelligent IoT edge solutions. The chapter starts with a detailed overview of machine learning and deep learning. Then the chapter moves to dynamic programming features and deep Q learning in IoT and Azure. Thereafter, the authors give a proposed model and detailed result and analysis.
Chapter 7 provides an improved fuzzy logic-based solutions for air conditioning systems. Then the authors give a proposed system which is composed of Fuzzy variables, Fuzzy base class, Fuzzy Rule Base and Fuzzy rule viewer. The chapter then gives the simulated results of the proposed system and conclusion is drawn based on it's novelty.
Chapter 8 has an important implementation to detect masked face to combat the pandemic situation. The chapter starts with a detailed related work on masked face recognition. Then it gives all the mathematical preliminaries required to understand the proposed system. Thereafter, it moves to the proposed system with algorithms, methods and applications followed by experimental results. It concludes with the novelty of the proposed system.
Chapter 9 is another deep learning approach to encounter COVID-19 pandemic situation. The chapter starts with the introduction of COVID19 situation and the need of deep learning-based solutions to encounter it. Here, the authors propose a medical imaging solution using deep learning where images of lungs are taken and COVID-19 infection positivity is predicted. The chapter also provides the method of COVID-19 variant tracing and biological protein structure. Moreover, this chapter gives selection drugs combination for a particular COVID-19 patient. It ends with detailed result and analysis of the proposed model.
Chapter 10 provides another online question answering system using Bengali language. It starts with the discussion on the existing literature then it moves towards a problem statement. The authors discuss the proposed model in a very structured way with algorithms. Thereafter, a detailed result and analysis is given and compared with existing work.
The chapter ends with analysis of error, some close observations, applications of the proposed model and scope for improvement as future work.
Part III: Security and Safety Aspects with Deep Learning
Chapter 11 gives a secure access mechanism for smart homes using biometric authentication and RFID authentication which can be implemented for IoT systems. The authors give a structured approach and framework for smart home access method with biometrics. The authors then discuss the same using RFID followed by proposed Control Scheme for Secure Access (CSFSC). The proposed system is discussed with mathematical equations and then it provides result of the proposed system.
Chapter 12 is a MQTT-based implementation of home automation system prototype with integrated Cyber-IoT infrastructure and also discusses deep learning-based security issues. After the introduction, literature review and importance of home automation, the author starts with proposed system architecture of home automation. Then it discusses the various security issues in home automation. Thereafter, the author moves to the implementation part of the proposed system and gives the detailed results with discussion.
Chapter 13 gives a malware detection framework using deep learning. Malware is a risk to the privacy of computer users which can cause an economic loss to organizations. Deep Learning is a subfield of machine learning which concentrates on human brains using artificial intelligence result analysis and conclusion.
Chapter 14 gives an application for women safety, namely "Patron for Women". The authors first give relative research where the first application is a mobile-based women safety application. Secondly, the authors refer to another application which is android-based. Thirdly, it gives another android-based application namely, "Lifecraft" and, finally, "Abhaya and Sakshi", another two-women safety applications are discussed. Then it provides a new methodology, system and model with deep learning for women safety. The...
