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<p>Preface xiii</p> <p><b>1 Industry 4.0: Smart Water Management System Using IoT 1<br /></b><i>S. Saravanan, N. Renugadevi, C.M. Naga Sudha and Parul Tripathi</i></p> <p>1.1 Introduction 2</p> <p>1.1.1 Industry 4.0 2</p> <p>1.1.2 IoT 2</p> <p>1.1.3 Smart City 3</p> <p>1.1.4 Smart Water Management 3</p> <p>1.2 Preliminaries 4</p> <p>1.2.1 Internet World to Intelligent World 4</p> <p>1.2.2 Architecture of IoT System 4</p> <p>1.2.3 Architecture of Smart City 6</p> <p>1.3 Literature Review on SWMS 7</p> <p>1.3.1 Water Quality Parameters Related to SWMS 8</p> <p>1.3.2 SWMS in Agriculture 8</p> <p>1.3.3 SWMS Using Smart Grids 9</p> <p>1.3.4 Machine Learning Models in SWMS 10</p> <p>1.3.5 IoT-Based SWMS 11</p> <p>1.4 Conclusion 11</p> <p>References 12</p> <p><b>2 Fourth Industrial Revolution Application: Network Forensics Cloud Security Issues 15<br /></b><i>Abdullah Ayub Khan, Asif Ali Laghari, Shafique Awan and Awais Khan Jumani</i></p> <p>2.1 Introduction 16</p> <p>2.1.1 Network Forensics 16</p> <p>2.1.2 The Fourth Industrial Revolution 17</p> <p>2.1.2.1 Machine-to-Machine (M2M) Communication 18</p> <p>2.1.3 Cloud Computing 18</p> <p>2.1.3.1 Infrastructure-as-a-Service (IaaS) 19</p> <p>2.1.3.2 Challenges of Cloud Security in Fourth Industrial Revolution 19</p> <p>2.2 Generic Model Architecture 20</p> <p>2.3 Model Implementation 24</p> <p>2.3.1 OpenNebula (Hypervisor) Implementation Platform 24</p> <p>2.3.2 NetworkMiner Analysis Tool 25</p> <p>2.3.3 Performance Matrix Evaluation & Result Discussion 27</p> <p>2.4 Cloud Security Impact on M2M Communication 28</p> <p>2.4.1 Cloud Computing Security Application in the Fourth Industrial Revolution (4.0) 29</p> <p>2.5 Conclusion 30</p> <p>References 31</p> <p><b>3 Regional Language Recognition System for Industry 4.0 35<br /></b><i>Bharathi V, N. Renugadevi, J. Padmapriya and M. Vijayprakash</i></p> <p>3.1 Introduction 36</p> <p>3.2 Automatic Speech Recognition System 39</p> <p>3.2.1 Preprocessing 41</p> <p>3.2.2 Feature Extraction 42</p> <p>3.2.2.1 Linear Predictive Coding (LPC) 42</p> <p>3.2.2.2 Linear Predictive Cepstral Coefficient (LPCC) 44</p> <p>3.2.2.3 Perceptual Linear Predictive (PLP) 44</p> <p>3.2.2.4 Power Spectral Analysis 44</p> <p>3.2.2.5 Mel Frequency Cepstral Coefficients 45</p> <p>3.2.2.6 Wavelet Transform 46</p> <p>3.2.3 Implementation of Deep Learning Technique 46</p> <p>3.2.3.1 Recurrent Neural Network 47</p> <p>3.2.3.2 Long Short-Term Memory Network 47</p> <p>3.2.3.3 Hidden Markov Models (HMM) 47</p> <p>3.2.3.4 Hidden Markov Models - Long Short-Term Memory Network (HMM-LSTM) 48</p> <p>3.2.3.5 Evaluation Metrics 49</p> <p>3.3 Literature Survey on Existing TSRS 49</p> <p>3.4 Conclusion 52</p> <p>References 52</p> <p><b>4 Approximation Algorithm and Linear Congruence: An Approach for Optimizing the Security of IoT-Based Healthcare Management System 55<br /></b><i>Anirban Bhowmik and Sunil Karforma</i></p> <p>4.1 Introduction 56</p> <p>4.1.1 IoT in Medical Devices 56</p> <p>4.1.2 Importance of Security and Privacy Protection in IoT-Based Healthcare System 57</p> <p>4.1.3 Cryptography and Secret Keys 58</p> <p>4.1.4 RSA 58</p> <p>4.1.5 Approximation Algorithm and Subset Sum Problem 58</p> <p>4.1.6 Significance of Use of Subset Sum Problem in Our Scheme 59</p> <p>4.1.7 Linear Congruence 60</p> <p>4.1.8 Linear and Non-Linear Functions 61</p> <p>4.1.9 Pell’s Equation 61</p> <p>4.2 Literature Survey 62</p> <p>4.3 Problem Domain 63</p> <p>4.4 Solution Domain and Objectives 64</p> <p>4.5 Proposed Work 65</p> <p>4.5.1 Methodology 65</p> <p>4.5.2 Session Key Generation 65</p> <p>4.5.3 Intermediate Key Generation 67</p> <p>4.5.4 Encryption Process 69</p> <p>4.5.5 Generation of Authentication Code and Transmission File 70</p> <p>4.5.6 Decryption Phase 71</p> <p>4.6 Results and Discussion 71</p> <p>4.6.1 Statistical Analysis 72</p> <p>4.6.2 Randomness Analysis of Key 73</p> <p>4.6.3 Key Sensitivity Analysis 75</p> <p>4.6.4 Security Analysis 76</p> <p>4.6.4.1 Key Space Analysis 76</p> <p>4.6.4.2 Brute-Force Attack 77</p> <p>4.6.4.3 Dictionary Attack 77</p> <p>4.6.4.4 Impersonation Attack 78</p> <p>4.6.4.5 Replay Attack 78</p> <p>4.6.4.6 Tampering Attack 78</p> <p>4.6.5 Comparative Analysis 79</p> <p>4.6.5.1 Comparative Analysis Related to IoT Attacks 79</p> <p>4.6.6 Significance of Authentication in Our Proposed Scheme 85</p> <p>4.7 Conclusion 85</p> <p>References 86</p> <p><b>5 A Hybrid Method for Fake Profile Detection in Social Network Using Artificial Intelligence 89<br /></b><i>Ajesh F, Aswathy S U, Felix M Philip and Jeyakrishnan V</i></p> <p>5.1 Introduction 90</p> <p>5.2 Literature Survey 91</p> <p>5.3 Methodology 94</p> <p>5.3.1 Datasets 94</p> <p>5.3.2 Detection of Fake Account 94</p> <p>5.3.3 Suggested Framework 95</p> <p>5.3.3.1 Pre-Processing 97</p> <p>5.3.3.2 Principal Component Analysis (PCA) 98</p> <p>5.3.3.3 Learning Algorithms 99</p> <p>5.3.3.4 Feature or Attribute Selection 102</p> <p>5.4 Result Analysis 103</p> <p>5.4.1 Cross-Validation 103</p> <p>5.4.2 Analysis of Metrics 104</p> <p>5.4.3 Performance Evaluation of Proposed Model 105</p> <p>5.4.4 Performance Analysis of Classifiers 105</p> <p>5.5 Conclusion 109</p> <p>References 109</p> <p><b>6 Packet Drop Detection in Agricultural-Based Internet of Things Platform 113<br /></b><i>Sebastian Terence and Geethanjali Purushothaman</i></p> <p>6.1 Introduction 113</p> <p>6.2 Problem Statement and Related Work 114</p> <p>6.3 Implementation of Packet Dropping Detection in IoT Platform 115</p> <p>6.4 Performance Analysis 120</p> <p>6.5 Conclusion 129</p> <p>References 129</p> <p><b>7 Smart Drone with Open CV to Clean the Railway Track 131<br /></b><i>Sujaritha M and Sujatha R</i></p> <p>7.1 Introduction 132</p> <p>7.2 Related Work 132</p> <p>7.3 Problem Definition 134</p> <p>7.4 The Proposed System 134</p> <p>7.4.1 Drones with Human Intervention 134</p> <p>7.4.2 Drones without Human Intervention 135</p> <p>7.4.3 Working Model 137</p> <p>7.5 Experimental Results 137</p> <p>7.6 Conclusion 139</p> <p>References 139</p> <p><b>8 Blockchain and Big Data: Supportive Aid for Daily Life 141<br /></b><i>Awais Khan Jumani, Asif Ali Laghari and Abdullah Ayub Khan</i></p> <p>8.1 Introduction 142</p> <p>8.1.1 Steps of Blockchain Technology Works 144</p> <p>8.1.2 Blockchain Private 144</p> <p>8.1.3 Blockchain Security 145</p> <p>8.2 Blockchain vs. Bitcoin 145</p> <p>8.2.1 Blockchain Applications 146</p> <p>8.2.2 Next Level of Blockchain 146</p> <p>8.2.3 Blockchain Architecture’s Basic Components 149</p> <p>8.2.4 Blockchain Architecture 150</p> <p>8.2.5 Blockchain Characteristics 150</p> <p>8.3 Blockchain Components 151</p> <p>8.3.1 Cryptography 152</p> <p>8.3.2 Distributed Ledger 153</p> <p>8.3.3 Smart Contracts 153</p> <p>8.3.4 Consensus Mechanism 154</p> <p>8.3.4.1 Proof of Work (PoW) 155</p> <p>8.3.4.2 Proof of Stake (PoS) 155</p> <p>8.4 Categories of Blockchain 155</p> <p>8.4.1 Public Blockchain 156</p> <p>8.4.2 Private Blockchain 156</p> <p>8.4.3 Consortium Blockchain 156</p> <p>8.4.4 Hybrid Blockchain 156</p> <p>8.5 Blockchain Applications 158</p> <p>8.5.1 Financial Application 158</p> <p>8.5.1.1 Bitcoin 158</p> <p>8.5.1.2 Ripple 158</p> <p>8.5.2 Non-Financial Applications 159</p> <p>8.5.2.1 Ethereum 159</p> <p>8.5.2.2 Hyperledger 159</p> <p>8.6 Blockchain in Different Sectors 160</p> <p>8.7 Blockchain Implementation Challenges 160</p> <p>8.8 Revolutionized Challenges in Industries 163</p> <p>8.9 Conclusion 170</p> <p>References 172</p> <p><b>9 A Novel Framework to Detect Effective Prediction Using Machine Learning 179<br /></b><i>Shenbaga Priya, Revadi, Sebastian Terence and Jude Immaculate</i></p> <p>9.1 Introduction 180</p> <p>9.2 ML-Based Prediction 180</p> <p>9.3 Prediction in Agriculture 182</p> <p>9.4 Prediction in Healthcare 183</p> <p>9.5 Prediction in Economics 184</p> <p>9.6 Prediction in Mammals 185</p> <p>9.7 Prediction in Weather 186</p> <p>9.8 Discussion 186</p> <p>9.9 Proposed Framework 187</p> <p>9.9.1 Problem Analysis 187</p> <p>9.9.2 Preprocessing 188</p> <p>9.9.3 Algorithm Selection 188</p> <p>9.9.4 Training the Machine 188</p> <p>9.9.5 Model Evaluation and Prediction 188</p> <p>9.9.6 Expert Suggestion 188</p> <p>9.9.7 Parameter Tuning 189</p> <p>9.10 Implementation 189</p> <p>9.10.1 Farmers and Sellers 189</p> <p>9.10.2 Products 189</p> <p>9.10.3 Price Prediction 190</p> <p>9.11 Conclusion 192</p> <p>References 192</p> <p><b>10 Dog Breed Classification Using CNN 195<br /></b><i>Sandra Varghese and Remya S</i></p> <p>10.1 Introduction 195</p> <p>10.2 Related Work 196</p> <p>10.3 Methodology 198</p> <p>10.4 Results and Discussions 201</p> <p>10.4.1 Training 201</p> <p>10.4.2 Testing 201</p> <p>10.5 Conclusions 203</p> <p>References 203</p> <p><b>11 Methodology for Load Balancing in Multi-Agent System Using SPE Approach 207<br /></b><i>S. Ajitha</i></p> <p>11.1 Introduction 207</p> <p>11.2 Methodology for Load Balancing 208</p> <p>11.3 Results and Discussion 213</p> <p>11.3.1 Proposed Algorithm in JADE Tool 213</p> <p>11.3.1.1 Sensitivity Analysis 218</p> <p>11.3.2 Proposed Algorithm in NetLogo 218</p> <p>11.4 Algorithms Used 219</p> <p>11.5 Results and Discussion 219</p> <p>11.6 Summary 226</p> <p>References 226</p> <p><b>12 The Impact of Cyber Culture on New Media Consumers 229<br /></b><i>Durmuş KoÇak</i></p> <p>12.1 Introduction 229</p> <p>12.2 The Rise of the Term of Cyber Culture 231</p> <p>12.2.1 Cyber Culture in the 21st Century 231</p> <p>12.2.1.1 Socio-Economic Results of Cyber Culture 232</p> <p>12.2.1.2 Psychological Outcomes of Cyber Culture 233</p> <p>12.2.1.3 Political Outcomes of Cyber Culture 234</p> <p>12.3 The Birth and Outcome of New Media Applications 234</p> <p>12.3.1 New Media Environments 236</p> <p>12.3.1.1 Social Sharing Networks 237</p> <p>12.3.1.2 Network Logs (Blog, Weblog) 240</p> <p>12.3.1.3 Computer Games 240</p> <p>12.3.1.4 Digital News Sites and Mobile Media 240</p> <p>12.3.1.5 Multimedia Media 241</p> <p>12.3.1.6 What Affects the New Media Consumers’ Tendencies? 242</p> <p>12.4 Result 244</p> <p>References 245</p> <p>Index 251</p>

<p><b>Shibin David</b> 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. </p> <p><b>R. S. Anand</b> 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. <p><b>V. Jeyakrishnan,</b> 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. <p><b>M. Niranjanamurthy,</b> 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.

<p><b>Written and edited by a team of international experts, this is the most comprehensive and up-to-date coverage of the security and privacy issues surrounding Industry 4.0 applications, a must-have for any library.</b></p> <p>The scope of Security Issues and Privacy Concerns in Industry 4.0 Applications is to envision the need for security in Industry 4.0 applications and the research opportunities for the future. This book discusses the security issues in Industry 4.0 applications for research development. It will also enable the reader to develop solutions for the security threats and attacks that prevail in the industry. The chapters will be framed on par with advancements in the industry in the area of Industry 4.0 with its applications in additive manufacturing, cloud computing, IoT (Internet of Things), and many others. This book helps a researcher and an industrial specialist to reflect on the latest trends and the need for technological change in Industry 4.0. <p>Smart water management using IoT, cloud security issues with network forensics, regional language recognition for industry 4.0, IoT-based health care management systems, artificial intelligence for fake profile detection, and packet drop detection in agriculture-based IoT are covered in this outstanding new volume. Leading innovations such as smart drone for railway track cleaning, everyday life-supporting blockchain and big data, effective prediction using machine learning, classification of dog breed based on CNN, load balancing using the SPE approach and cyber culture impact on media consumers are also addressed. <p>Whether a reference for the veteran engineer or an introduction to the technologies covered in the book for the student, this is a must-have for any library.

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