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Convergence of Deep Learning in Cyber-IoT Systems and Security

Artificial Intelligence and Soft Computing for Industrial Transformation 1. Aufl.

von: Rajdeep Chakraborty, Anupam Ghosh, Jyotsna Kumar Mandal, S. Balamurugan
173,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	24.10.2022
ISBN/EAN:	9781119857679
Sprache:	englisch
Anzahl Seiten:	480

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Titelbeschreibung

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 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

Autorenportrait

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.

Back cover copy

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.