Security Issues and Privacy Concerns in Industry 4.0 Applications

ISBN-13: 9781119775621 / Angielski / Twarda / 2021 / 272 str.

Spis treści

Preface xiii1 Industry 4.0: Smart Water Management System Using IoT 1S. Saravanan, N. Renugadevi, C.M. Naga Sudha and Parul Tripathi1.1 Introduction 21.1.1 Industry 4.0 21.1.2 IoT 21.1.3 Smart City 31.1.4 Smart Water Management 31.2 Preliminaries 41.2.1 Internet World to Intelligent World 41.2.2 Architecture of IoT System 41.2.3 Architecture of Smart City 61.3 Literature Review on SWMS 71.3.1 Water Quality Parameters Related to SWMS 81.3.2 SWMS in Agriculture 81.3.3 SWMS Using Smart Grids 91.3.4 Machine Learning Models in SWMS 101.3.5 IoT-Based SWMS 111.4 Conclusion 11References 122 Fourth Industrial Revolution Application: Network Forensics Cloud Security Issues 15Abdullah Ayub Khan, Asif Ali Laghari, Shafique Awan and Awais Khan Jumani2.1 Introduction 162.1.1 Network Forensics 162.1.2 The Fourth Industrial Revolution 172.1.2.1 Machine-to-Machine (M2M) Communication 182.1.3 Cloud Computing 182.1.3.1 Infrastructure-as-a-Service (IaaS) 192.1.3.2 Challenges of Cloud Security in Fourth Industrial Revolution 192.2 Generic Model Architecture 202.3 Model Implementation 242.3.1 OpenNebula (Hypervisor) Implementation Platform 242.3.2 NetworkMiner Analysis Tool 252.3.3 Performance Matrix Evaluation & Result Discussion 272.4 Cloud Security Impact on M2M Communication 282.4.1 Cloud Computing Security Application in the Fourth Industrial Revolution (4.0) 292.5 Conclusion 30References 313 Regional Language Recognition System for Industry 4.0 35Bharathi V, N. Renugadevi, J. Padmapriya and M. Vijayprakash3.1 Introduction 363.2 Automatic Speech Recognition System 393.2.1 Preprocessing 413.2.2 Feature Extraction 423.2.2.1 Linear Predictive Coding (LPC) 423.2.2.2 Linear Predictive Cepstral Coefficient (LPCC) 443.2.2.3 Perceptual Linear Predictive (PLP) 443.2.2.4 Power Spectral Analysis 443.2.2.5 Mel Frequency Cepstral Coefficients 453.2.2.6 Wavelet Transform 463.2.3 Implementation of Deep Learning Technique 463.2.3.1 Recurrent Neural Network 473.2.3.2 Long Short-Term Memory Network 473.2.3.3 Hidden Markov Models (HMM) 473.2.3.4 Hidden Markov Models - Long Short-Term Memory Network (HMM-LSTM) 483.2.3.5 Evaluation Metrics 493.3 Literature Survey on Existing TSRS 493.4 Conclusion 52References 524 Approximation Algorithm and Linear Congruence: An Approach for Optimizing the Security of IoT-Based Healthcare Management System 55Anirban Bhowmik and Sunil Karforma4.1 Introduction 564.1.1 IoT in Medical Devices 564.1.2 Importance of Security and Privacy Protection in IoT-Based Healthcare System 574.1.3 Cryptography and Secret Keys 584.1.4 RSA 584.1.5 Approximation Algorithm and Subset Sum Problem 584.1.6 Significance of Use of Subset Sum Problem in Our Scheme 594.1.7 Linear Congruence 604.1.8 Linear and Non-Linear Functions 614.1.9 Pell's Equation 614.2 Literature Survey 624.3 Problem Domain 634.4 Solution Domain and Objectives 644.5 Proposed Work 654.5.1 Methodology 654.5.2 Session Key Generation 654.5.3 Intermediate Key Generation 674.5.4 Encryption Process 694.5.5 Generation of Authentication Code and Transmission File 704.5.6 Decryption Phase 714.6 Results and Discussion 714.6.1 Statistical Analysis 724.6.2 Randomness Analysis of Key 734.6.3 Key Sensitivity Analysis 754.6.4 Security Analysis 764.6.4.1 Key Space Analysis 764.6.4.2 Brute-Force Attack 774.6.4.3 Dictionary Attack 774.6.4.4 Impersonation Attack 784.6.4.5 Replay Attack 784.6.4.6 Tampering Attack 784.6.5 Comparative Analysis 794.6.5.1 Comparative Analysis Related to IoT Attacks 794.6.6 Significance of Authentication in Our Proposed Scheme 854.7 Conclusion 85References 865 A Hybrid Method for Fake Profile Detection in Social Network Using Artificial Intelligence 89Ajesh F, Aswathy S U, Felix M Philip and Jeyakrishnan V5.1 Introduction 905.2 Literature Survey 915.3 Methodology 945.3.1 Datasets 945.3.2 Detection of Fake Account 945.3.3 Suggested Framework 955.3.3.1 Pre-Processing 975.3.3.2 Principal Component Analysis (PCA) 985.3.3.3 Learning Algorithms 995.3.3.4 Feature or Attribute Selection 1025.4 Result Analysis 1035.4.1 Cross-Validation 1035.4.2 Analysis of Metrics 1045.4.3 Performance Evaluation of Proposed Model 1055.4.4 Performance Analysis of Classifiers 1055.5 Conclusion 109References 1096 Packet Drop Detection in Agricultural-Based Internet of Things Platform 113Sebastian Terence and Geethanjali Purushothaman6.1 Introduction 1136.2 Problem Statement and Related Work 1146.3 Implementation of Packet Dropping Detection in IoT Platform 1156.4 Performance Analysis 1206.5 Conclusion 129References 1297 Smart Drone with Open CV to Clean the Railway Track 131Sujaritha M and Sujatha R7.1 Introduction 1327.2 Related Work 1327.3 Problem Definition 1347.4 The Proposed System 1347.4.1 Drones with Human Intervention 1347.4.2 Drones without Human Intervention 1357.4.3 Working Model 1377.5 Experimental Results 1377.6 Conclusion 139References 1398 Blockchain and Big Data: Supportive Aid for Daily Life 141Awais Khan Jumani, Asif Ali Laghari and Abdullah Ayub Khan8.1 Introduction 1428.1.1 Steps of Blockchain Technology Works 1448.1.2 Blockchain Private 1448.1.3 Blockchain Security 1458.2 Blockchain vs. Bitcoin 1458.2.1 Blockchain Applications 1468.2.2 Next Level of Blockchain 1468.2.3 Blockchain Architecture's Basic Components 1498.2.4 Blockchain Architecture 1508.2.5 Blockchain Characteristics 1508.3 Blockchain Components 1518.3.1 Cryptography 1528.3.2 Distributed Ledger 1538.3.3 Smart Contracts 1538.3.4 Consensus Mechanism 1548.3.4.1 Proof of Work (PoW) 1558.3.4.2 Proof of Stake (PoS) 1558.4 Categories of Blockchain 1558.4.1 Public Blockchain 1568.4.2 Private Blockchain 1568.4.3 Consortium Blockchain 1568.4.4 Hybrid Blockchain 1568.5 Blockchain Applications 1588.5.1 Financial Application 1588.5.1.1 Bitcoin 1588.5.1.2 Ripple 1588.5.2 Non-Financial Applications 1598.5.2.1 Ethereum 1598.5.2.2 Hyperledger 1598.6 Blockchain in Different Sectors 1608.7 Blockchain Implementation Challenges 1608.8 Revolutionized Challenges in Industries 1638.9 Conclusion 170References 1729 A Novel Framework to Detect Effective Prediction Using Machine Learning 179Shenbaga Priya, Revadi, Sebastian Terence and Jude Immaculate9.1 Introduction 1809.2 ML-Based Prediction 1809.3 Prediction in Agriculture 1829.4 Prediction in Healthcare 1839.5 Prediction in Economics 1849.6 Prediction in Mammals 1859.7 Prediction in Weather 1869.8 Discussion 1869.9 Proposed Framework 1879.9.1 Problem Analysis 1879.9.2 Preprocessing 1889.9.3 Algorithm Selection 1889.9.4 Training the Machine 1889.9.5 Model Evaluation and Prediction 1889.9.6 Expert Suggestion 1889.9.7 Parameter Tuning 1899.10 Implementation 1899.10.1 Farmers and Sellers 1899.10.2 Products 1899.10.3 Price Prediction 1909.11 Conclusion 192References 19210 Dog Breed Classification Using CNN 195Sandra Varghese and Remya S10.1 Introduction 19510.2 Related Work 19610.3 Methodology 19810.4 Results and Discussions 20110.4.1 Training 20110.4.2 Testing 20110.5 Conclusions 203References 20311 Methodology for Load Balancing in Multi-Agent System Using SPE Approach 207S. Ajitha11.1 Introduction 20711.2 Methodology for Load Balancing 20811.3 Results and Discussion 21311.3.1 Proposed Algorithm in JADE Tool 21311.3.1.1 Sensitivity Analysis 21811.3.2 Proposed Algorithm in NetLogo 21811.4 Algorithms Used 21911.5 Results and Discussion 21911.6 Summary 226References 22612 The Impact of Cyber Culture on New Media Consumers 229Durmu_ KoÇak12.1 Introduction 22912.2 The Rise of the Term of Cyber Culture 23112.2.1 Cyber Culture in the 21st Century 23112.2.1.1 Socio-Economic Results of Cyber Culture 23212.2.1.2 Psychological Outcomes of Cyber Culture 23312.2.1.3 Political Outcomes of Cyber Culture 23412.3 The Birth and Outcome of New Media Applications 23412.3.1 New Media Environments 23612.3.1.1 Social Sharing Networks 23712.3.1.2 Network Logs (Blog, Weblog) 24012.3.1.3 Computer Games 24012.3.1.4 Digital News Sites and Mobile Media 24012.3.1.5 Multimedia Media 24112.3.1.6 What Affects the New Media Consumers' Tendencies? 24212.4 Result 244References 245Index 251

O autorze

Shibin David is an assistant professor in the Department of Computer Science and Engineering at Karunya Institute of Technology and Sciences, India. His research interest includes cryptography, network security and mobile computing. He has an industry certification from Oracle, several awards, and a number of publications to his credit.R. S. Anand is a researcher in the field of mechanical engineering at the Karunya Institute of Technology and Sciences, India, after being an assistant professor at the Narayana Guru College of Engineering from 2014 to 2016. He has numerous papers and presentations to his credit.V. Jeyakrishnan, PhD, is an assistant professor at Saintgits College of Engineering, Kottayam, India. His research area includes wireless networks, cloud computing and its applications. He has a number of publications in his research area.M. Niranjanamurthy, PhD, is an assistant professor in the Department of Computer Applications, M S Ramaiah Institute of Technology, Bangalore, Karnataka. He received his doctorate in computer science from JJTU, Rajasthan. He has over ten years of teaching experience and two years of industry experience as a software engineer. He has published four books, 70 papers, and has filed for 17 Patents with three so far granted. He is a reviewer for 22 international academic journals and has twice won Best Research Journal Reviewer award. He has numerous other awards and in is active in research associations.