{"product_id":"convergence-of-deep-learning-in-cyber-iot-systems-and-security-hardback-9781119857211","title":"Convergence of Deep Learning in Cyber-IoT Systems and Security (Hardback) 9781119857211","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eConvergence of Deep Learning in Cyber-IoT Systems and Security\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eRajdeep Chakraborty (Edited by), R Chakraborty (Author), Anupam Ghosh (Edited by), Jyotsna Kumar Mandal (Edited by), S. Balamurugan (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119857211, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 9 December 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e480 pages\u003cbr\u003e22.9 x 15.2 x 2.9 cm, 0.907 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eCONVERGENCE OF DEEP LEARNING IN CYBER-IOT SYSTEMS AND SECURITY\u003c\/b\u003e \u003cp\u003e\u003cb\u003eIn-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. \u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe 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. \u003c\/p\u003e\n\u003cp\u003eThis 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. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eResearchers and industry engineers in computer science, information technology, electronics and communication, cybersecurity and cryptography.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Various Approaches from Machine Learning to Deep Learning 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Web-Assisted Noninvasive Detection of Oral Submucous Fibrosis Using IoHT 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnimesh Upadhyaya, Vertika Rai, Debdutta Pal, Surajit Bose and Somnath Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Literature Survey 6\u003c\/p\u003e \u003cp\u003e1.2.1 Oral Cancer 6\u003c\/p\u003e \u003cp\u003e1.3 Primary Concepts 7\u003c\/p\u003e \u003cp\u003e1.3.1 Transmission Efficiency 7\u003c\/p\u003e \u003cp\u003e1.4 Propose Model 9\u003c\/p\u003e \u003cp\u003e1.4.1 Platform Configuration 9\u003c\/p\u003e \u003cp\u003e1.4.2 Harvard Architectural Microcontroller Base Wireless Communication Board 10\u003c\/p\u003e \u003cp\u003e1.4.2.1 NodeMCU ESP8266 Microcontroller 10\u003c\/p\u003e \u003cp\u003e1.4.2.2 Gas Sensor 12\u003c\/p\u003e \u003cp\u003e1.4.3 Experimental Setup 13\u003c\/p\u003e \u003cp\u003e1.4.4 Process to Connect to Sever and Analyzing Data on Cloud 14\u003c\/p\u003e \u003cp\u003e1.5 Comparative Study 16\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 17\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Performance Evaluation of Machine Learning and Deep Learning Techniques: A Comparative Analysis for House Price Prediction 21\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSajeev Ram Arumugam, Sheela Gowr, Abimala, Balakrishna and Oswalt Manoj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 22\u003c\/p\u003e \u003cp\u003e2.2 Related Research 23\u003c\/p\u003e \u003cp\u003e2.2.1 Literature Review on Comparing the Performance of the ML\/DL Algorithms 23\u003c\/p\u003e \u003cp\u003e2.2.2 Literature Review on House Price Prediction 25\u003c\/p\u003e \u003cp\u003e2.3 Research Methodology 26\u003c\/p\u003e \u003cp\u003e2.3.1 Data Collection 27\u003c\/p\u003e \u003cp\u003e2.3.2 Data Visualization 27\u003c\/p\u003e \u003cp\u003e2.3.3 Data Preparation 28\u003c\/p\u003e \u003cp\u003e2.3.4 Regression Models 29\u003c\/p\u003e \u003cp\u003e2.3.4.1 Simple Linear Regression 29\u003c\/p\u003e \u003cp\u003e2.3.4.2 Random Forest Regression 30\u003c\/p\u003e \u003cp\u003e2.3.4.3 Ada Boosting Regression 31\u003c\/p\u003e \u003cp\u003e2.3.4.4 Gradient Boosting Regression 32\u003c\/p\u003e \u003cp\u003e2.3.4.5 Support Vector Regression 33\u003c\/p\u003e \u003cp\u003e2.3.4.6 Artificial Neural Network 34\u003c\/p\u003e \u003cp\u003e2.3.4.7 Multioutput Regression 36\u003c\/p\u003e \u003cp\u003e2.3.4.8 Regression Using Tensorflow—Keras 37\u003c\/p\u003e \u003cp\u003e2.3.5 Classification Models 39\u003c\/p\u003e \u003cp\u003e2.3.5.1 Logistic Regression Classifier 39\u003c\/p\u003e \u003cp\u003e2.3.5.2 Decision Tree Classifier 39\u003c\/p\u003e \u003cp\u003e2.3.5.3 Random Forest Classifier 41\u003c\/p\u003e \u003cp\u003e2.3.5.4 Naïve Bayes Classifier 41\u003c\/p\u003e \u003cp\u003e2.3.5.5 K-Nearest Neighbors Classifier 42\u003c\/p\u003e \u003cp\u003e2.3.5.6 Support Vector Machine Classifier (SVM) 43\u003c\/p\u003e \u003cp\u003e2.3.5.7 Feed Forward Neural Network 43\u003c\/p\u003e \u003cp\u003e2.3.5.8 Recurrent Neural Networks 44\u003c\/p\u003e \u003cp\u003e2.3.5.9 LSTM Recurrent Neural Networks 44\u003c\/p\u003e \u003cp\u003e2.3.6 Performance Metrics for Regression Models 45\u003c\/p\u003e \u003cp\u003e2.3.7 Performance Metrics for Classification Models 46\u003c\/p\u003e \u003cp\u003e2.4 Experimentation 47\u003c\/p\u003e \u003cp\u003e2.5 Results and Discussion 48\u003c\/p\u003e \u003cp\u003e2.6 Suggestions 60\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 60\u003c\/p\u003e \u003cp\u003eReferences 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Cyber Physical Systems, Machine Learning \u0026amp; Deep Learning— Emergence as an Academic Program and Field for Developing Digital Society 67\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eP. K. Paul\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 68\u003c\/p\u003e \u003cp\u003e3.2 Objective of the Work 69\u003c\/p\u003e \u003cp\u003e3.3 Methods 69\u003c\/p\u003e \u003cp\u003e3.4 Cyber Physical Systems: Overview with Emerging Academic Potentiality 70\u003c\/p\u003e \u003cp\u003e3.5 ml and dl Basics with Educational Potentialities 72\u003c\/p\u003e \u003cp\u003e3.5.1 Machine Learning (ML) 72\u003c\/p\u003e \u003cp\u003e3.5.2 Deep Learning 73\u003c\/p\u003e \u003cp\u003e3.6 Manpower and Developing Scenario in Machine Learning and Deep Learning 74\u003c\/p\u003e \u003cp\u003e3.7 dl \u0026amp; ml in Indian Context 79\u003c\/p\u003e \u003cp\u003e3.8 Conclusion 81\u003c\/p\u003e \u003cp\u003eReferences 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Detection of Fake News and Rumors in the Social Media Using Machine Learning Techniques With Semantic Attributes 85\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDiganta Saha, Arijit Das, Tanmay Chandra Nath, Soumyadip Saha and Ratul Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 86\u003c\/p\u003e \u003cp\u003e4.2 Literature Survey 87\u003c\/p\u003e \u003cp\u003e4.3 Proposed Work 88\u003c\/p\u003e \u003cp\u003e4.3.1 Algorithm 89\u003c\/p\u003e \u003cp\u003e4.3.2 Flowchart 90\u003c\/p\u003e \u003cp\u003e4.3.3 Explanation of Approach 91\u003c\/p\u003e \u003cp\u003e4.4 Results and Analysis 92\u003c\/p\u003e \u003cp\u003e4.4.1 Datasets 92\u003c\/p\u003e \u003cp\u003e4.4.2 Evaluation 93\u003c\/p\u003e \u003cp\u003e4.4.2.1 Result of 1st Dataset 93\u003c\/p\u003e \u003cp\u003e4.4.2.2 Result of 2nd Dataset 94\u003c\/p\u003e \u003cp\u003e4.4.2.3 Result of 3rd Dataset 94\u003c\/p\u003e \u003cp\u003e4.4.3 Relative Comparison of Performance 95\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 95\u003c\/p\u003e \u003cp\u003eReferences 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Innovative Solutions Based on Deep Learning 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Online Assessment System Using Natural Language Processing Techniques 101\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Suriya, K. Nagalakshmi and Nivetha S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 102\u003c\/p\u003e \u003cp\u003e5.2 Literature Survey 103\u003c\/p\u003e \u003cp\u003e5.3 Existing Algorithms 108\u003c\/p\u003e \u003cp\u003e5.4 Proposed System Design 111\u003c\/p\u003e \u003cp\u003e5.5 System Implementation 115\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 120\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 On a Reference Architecture to Build Deep-Q Learning-Based Intelligent IoT Edge Solutions 123\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmit Chakraborty, Ankit Kumar Shaw and Sucharita Samanta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 124\u003c\/p\u003e \u003cp\u003e6.1.1 A Brief Primer on Machine Learning 124\u003c\/p\u003e \u003cp\u003e6.1.1.1 Types of Machine Learning 124\u003c\/p\u003e \u003cp\u003e6.2 Dynamic Programming 128\u003c\/p\u003e \u003cp\u003e6.3 Deep Q-Learning 129\u003c\/p\u003e \u003cp\u003e6.4 IoT 130\u003c\/p\u003e \u003cp\u003e6.4.1 Azure 130\u003c\/p\u003e \u003cp\u003e6.4.1.1 IoT on Azure 130\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 144\u003c\/p\u003e \u003cp\u003e6.6 Future Work 144\u003c\/p\u003e \u003cp\u003eReferences 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Fuzzy Logic-Based Air Conditioner System 147\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSuparna Biswas, Sayan Roy Chaudhuri, Ayusha Biswas and Arpan Bhawal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 147\u003c\/p\u003e \u003cp\u003e7.2 Fuzzy Logic-Based Control System 149\u003c\/p\u003e \u003cp\u003e7.3 Proposed System 149\u003c\/p\u003e \u003cp\u003e7.3.1 Fuzzy Variables 149\u003c\/p\u003e \u003cp\u003e7.3.2 Fuzzy Base Class 154\u003c\/p\u003e \u003cp\u003e7.3.3 Fuzzy Rule Base 155\u003c\/p\u003e \u003cp\u003e7.3.4 Fuzzy Rule Viewer 156\u003c\/p\u003e \u003cp\u003e7.4 Simulated Result 157\u003c\/p\u003e \u003cp\u003e7.5 Conclusion and Future Work 163\u003c\/p\u003e \u003cp\u003eReferences 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 An Efficient Masked-Face Recognition Technique to Combat with COVID- 19 165\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSuparna Biswas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 165\u003c\/p\u003e \u003cp\u003e8.2 Related Works 167\u003c\/p\u003e \u003cp\u003e8.2.1 Review of Face Recognition for Unmasked Faces 167\u003c\/p\u003e \u003cp\u003e8.2.2 Review of Face Recognition for Masked Faces 168\u003c\/p\u003e \u003cp\u003e8.3 Mathematical Preliminaries 169\u003c\/p\u003e \u003cp\u003e8.3.1 Digital Curvelet Transform (DCT) 169\u003c\/p\u003e \u003cp\u003e8.3.2 Compressive Sensing–Based Classification 170\u003c\/p\u003e \u003cp\u003e8.4 Proposed Method 171\u003c\/p\u003e \u003cp\u003e8.5 Experimental Results 173\u003c\/p\u003e \u003cp\u003e8.5.1 Database 173\u003c\/p\u003e \u003cp\u003e8.5.2 Result 175\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 179\u003c\/p\u003e \u003cp\u003eReferences 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Deep Learning: An Approach to Encounter Pandemic Effect of Novel Corona Virus (COVID-19) 183\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSantanu Koley, Pinaki Pratim Acharjya, Rajesh Mukherjee, Soumitra Roy and Somdeep Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 184\u003c\/p\u003e \u003cp\u003e9.2 Interpretation With Medical Imaging 185\u003c\/p\u003e \u003cp\u003e9.3 Corona Virus Variants Tracing 188\u003c\/p\u003e \u003cp\u003e9.4 Spreading Capability and Destructiveness of Virus 191\u003c\/p\u003e \u003cp\u003e9.5 Deduction of Biological Protein Structure 192\u003c\/p\u003e \u003cp\u003e9.6 Pandemic Model Structuring and Recommended Drugs 192\u003c\/p\u003e \u003cp\u003e9.7 Selection of Medicine 195\u003c\/p\u003e \u003cp\u003e9.8 Result Analysis 197\u003c\/p\u003e \u003cp\u003e9.9 Conclusion 201\u003c\/p\u003e \u003cp\u003eReferences 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Question Answering System Using Deep Learning in the Low Resource Language Bengali 207\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArijit Das and Diganta Saha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 208\u003c\/p\u003e \u003cp\u003e10.2 Related Work 210\u003c\/p\u003e \u003cp\u003e10.3 Problem Statement 215\u003c\/p\u003e \u003cp\u003e10.4 Proposed Approach 215\u003c\/p\u003e \u003cp\u003e10.5 Algorithm 216\u003c\/p\u003e \u003cp\u003e10.6 Results and Discussion 219\u003c\/p\u003e \u003cp\u003e10.6.1 Result Summary for TDIL Dataset 219\u003c\/p\u003e \u003cp\u003e10.6.2 Result Summary for SQuAD Dataset 219\u003c\/p\u003e \u003cp\u003e10.6.3 Examples of Retrieved Answers 220\u003c\/p\u003e \u003cp\u003e10.6.4 Calculation of TP, TN, FP, FN, Accuracy, Precision, Recall, and F1 score 221\u003c\/p\u003e \u003cp\u003e10.6.5 Comparison of Result with other Methods and Dataset 222\u003c\/p\u003e \u003cp\u003e10.7 Analysis of Error 223\u003c\/p\u003e \u003cp\u003e10.8 Few Close Observations 223\u003c\/p\u003e \u003cp\u003e10.9 Applications 224\u003c\/p\u003e \u003cp\u003e10.10 Scope for Improvements 224\u003c\/p\u003e \u003cp\u003e10.11 Conclusions 224\u003c\/p\u003e \u003cp\u003eAcknowledgments 225\u003c\/p\u003e \u003cp\u003eReferences 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Security and Safety Aspects with Deep Learning 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Secure Access to Smart Homes Using Biometric Authentication With RFID Reader for IoT Systems 233\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eK.S. Niraja and Sabbineni Srinivasa Rao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 234\u003c\/p\u003e \u003cp\u003e11.2 Related Work 235\u003c\/p\u003e \u003cp\u003e11.3 Framework for Smart Home Use Case With Biometric 236\u003c\/p\u003e \u003cp\u003e11.3.1 RFID-Based Authentication and Its Drawbacks 236\u003c\/p\u003e \u003cp\u003e11.4 Control Scheme for Secure Access (CSFSC) 237\u003c\/p\u003e \u003cp\u003e11.4.1 Problem Definition 237\u003c\/p\u003e \u003cp\u003e11.4.2 Biometric-Based RFID Reader Proposed Scheme 238\u003c\/p\u003e \u003cp\u003e11.4.3 Reader-Based Procedures 240\u003c\/p\u003e \u003cp\u003e11.4.4 Backend Server-Side Procedures 240\u003c\/p\u003e \u003cp\u003e11.4.5 Reader Side Final Compute and Check Operations 240\u003c\/p\u003e \u003cp\u003e11.5 Results Observed Based on Various Features With Proposed and Existing Methods 242\u003c\/p\u003e \u003cp\u003e11.6 Conclusions and Future Work 245\u003c\/p\u003e \u003cp\u003eReferences 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 MQTT-Based Implementation of Home Automation System Prototype With Integrated Cyber-IoT Infrastructure and Deep Learning–Based Security Issues 249\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArnab Chakraborty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 250\u003c\/p\u003e \u003cp\u003e12.2 Architecture of Implemented Home Automation 252\u003c\/p\u003e \u003cp\u003e12.3 Challenges in Home Automation 253\u003c\/p\u003e \u003cp\u003e12.3.1 Distributed Denial of Service and Attack 254\u003c\/p\u003e \u003cp\u003e12.3.2 Deep Learning–Based Solution Aspects 254\u003c\/p\u003e \u003cp\u003e12.4 Implementation 255\u003c\/p\u003e \u003cp\u003e12.4.1 Relay 256\u003c\/p\u003e \u003cp\u003e12.4.2 DHT 11 257\u003c\/p\u003e \u003cp\u003e12.5 Results and Discussions 262\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 265\u003c\/p\u003e \u003cp\u003eReferences 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Malware Detection in Deep Learning 269\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSharmila Gaikwad and Jignesh Patil\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction to Malware 270\u003c\/p\u003e \u003cp\u003e13.1.1 Computer Security 270\u003c\/p\u003e \u003cp\u003e13.1.2 What Is Malware? 271\u003c\/p\u003e \u003cp\u003e13.2 Machine Learning and Deep Learning for Malware Detection 274\u003c\/p\u003e \u003cp\u003e13.2.1 Introduction to Machine Learning 274\u003c\/p\u003e \u003cp\u003e13.2.2 Introduction to Deep Learning 276\u003c\/p\u003e \u003cp\u003e13.2.3 Detection Techniques Using Deep Learning 279\u003c\/p\u003e \u003cp\u003e13.3 Case Study on Malware Detection 280\u003c\/p\u003e \u003cp\u003e13.3.1 Impact of Malware on Systems 280\u003c\/p\u003e \u003cp\u003e13.3.2 Effect of Malware in a Pandemic Situation 281\u003c\/p\u003e \u003cp\u003e13.4 Conclusion 283\u003c\/p\u003e \u003cp\u003eReferences 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Patron for Women: An Application for Womens Safety 285\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRiya Sil, Snatam Kamila, Ayan Mondal, Sufal Paul, Santanu Sinha and Bishes Saha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 286\u003c\/p\u003e \u003cp\u003e14.2 Background Study 286\u003c\/p\u003e \u003cp\u003e14.3 Related Research 287\u003c\/p\u003e \u003cp\u003e14.3.1 A Mobile-Based Women Safety Application (I safe App) 287\u003c\/p\u003e \u003cp\u003e14.3.2 Lifecraft: An Android-Based Application System for Women Safety 288\u003c\/p\u003e \u003cp\u003e14.3.3 Abhaya: An Android App for the Safety of Women 288\u003c\/p\u003e \u003cp\u003e14.3.4 Sakhi—The Saviour: An Android Application to Help Women in Times of Social Insecurity 289\u003c\/p\u003e \u003cp\u003e14.4 Proposed Methodology 289\u003c\/p\u003e \u003cp\u003e14.4.1 Motivation and Objective 290\u003c\/p\u003e \u003cp\u003e14.4.2 Proposed System 290\u003c\/p\u003e \u003cp\u003e14.4.3 System Flowchart 291\u003c\/p\u003e \u003cp\u003e14.4.4 Use-Case Model 291\u003c\/p\u003e \u003cp\u003e14.4.5 Novelty of the Work 294\u003c\/p\u003e \u003cp\u003e14.4.6 Comparison with Existing System 294\u003c\/p\u003e \u003cp\u003e14.5 Results and Analysis 294\u003c\/p\u003e \u003cp\u003e14.6 Conclusion and Future Work 298\u003c\/p\u003e \u003cp\u003eReferences 299\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Concepts and Techniques in Deep Learning Applications in the Field of IoT Systems and Security 303\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSantanu Koley and Pinaki Pratim Acharjya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 304\u003c\/p\u003e \u003cp\u003e15.2 Concepts of Deep Learning 307\u003c\/p\u003e \u003cp\u003e15.3 Techniques of Deep Learning 308\u003c\/p\u003e \u003cp\u003e15.3.1 Classic Neural Networks 309\u003c\/p\u003e \u003cp\u003e15.3.1.1 Linear Function 309\u003c\/p\u003e \u003cp\u003e15.3.1.2 Nonlinear Function 309\u003c\/p\u003e \u003cp\u003e15.3.1.3 Sigmoid Curve 310\u003c\/p\u003e \u003cp\u003e15.3.1.4 Rectified Linear Unit 310\u003c\/p\u003e \u003cp\u003e15.3.2 Convolution Neural Networks 310\u003c\/p\u003e \u003cp\u003e15.3.2.1 Convolution 311\u003c\/p\u003e \u003cp\u003e15.3.2.2 Max-Pooling 311\u003c\/p\u003e \u003cp\u003e15.3.2.3 Flattening 311\u003c\/p\u003e \u003cp\u003e15.3.2.4 Full Connection 311\u003c\/p\u003e \u003cp\u003e15.3.3 Recurrent Neural Networks 312\u003c\/p\u003e \u003cp\u003e15.3.3.1 LSTMs 312\u003c\/p\u003e \u003cp\u003e15.3.3.2 Gated RNNs 312\u003c\/p\u003e \u003cp\u003e15.3.4 Generative Adversarial Networks 313\u003c\/p\u003e \u003cp\u003e15.3.5 Self-Organizing Maps 314\u003c\/p\u003e \u003cp\u003e15.3.6 Boltzmann Machines 315\u003c\/p\u003e \u003cp\u003e15.3.7 Deep Reinforcement Learning 315\u003c\/p\u003e \u003cp\u003e15.3.8 Auto Encoders 316\u003c\/p\u003e \u003cp\u003e15.3.8.1 Sparse 317\u003c\/p\u003e \u003cp\u003e15.3.8.2 Denoising 317\u003c\/p\u003e \u003cp\u003e15.3.8.3 Contractive 317\u003c\/p\u003e \u003cp\u003e15.3.8.4 Stacked 317\u003c\/p\u003e \u003cp\u003e15.3.9 Back Propagation 317\u003c\/p\u003e \u003cp\u003e15.3.10 Gradient Descent 318\u003c\/p\u003e \u003cp\u003e15.4 Deep Learning Applications 319\u003c\/p\u003e \u003cp\u003e15.4.1 Automatic Speech Recognition (ASR) 319\u003c\/p\u003e \u003cp\u003e15.4.2 Image Recognition 320\u003c\/p\u003e \u003cp\u003e15.4.3 Natural Language Processing 320\u003c\/p\u003e \u003cp\u003e15.4.4 Drug Discovery and Toxicology 321\u003c\/p\u003e \u003cp\u003e15.4.5 Customer Relationship Management 322\u003c\/p\u003e \u003cp\u003e15.4.6 Recommendation Systems 323\u003c\/p\u003e \u003cp\u003e15.4.7 Bioinformatics 324\u003c\/p\u003e \u003cp\u003e15.5 Concepts of IoT Systems 325\u003c\/p\u003e \u003cp\u003e15.6 Techniques of IoT Systems 326\u003c\/p\u003e \u003cp\u003e15.6.1 Architecture 326\u003c\/p\u003e \u003cp\u003e15.6.2 Programming Model 327\u003c\/p\u003e \u003cp\u003e15.6.3 Scheduling Policy 329\u003c\/p\u003e \u003cp\u003e15.6.4 Memory Footprint 329\u003c\/p\u003e \u003cp\u003e15.6.5 Networking 332\u003c\/p\u003e \u003cp\u003e15.6.6 Portability 332\u003c\/p\u003e \u003cp\u003e15.6.7 Energy Efficiency 333\u003c\/p\u003e \u003cp\u003e15.7 IoT Systems Applications 333\u003c\/p\u003e \u003cp\u003e15.7.1 Smart Home 334\u003c\/p\u003e \u003cp\u003e15.7.2 Wearables 335\u003c\/p\u003e \u003cp\u003e15.7.3 Connected Cars 335\u003c\/p\u003e \u003cp\u003e15.7.4 Industrial Internet 336\u003c\/p\u003e \u003cp\u003e15.7.5 Smart Cities 337\u003c\/p\u003e \u003cp\u003e15.7.6 IoT in Agriculture 337\u003c\/p\u003e \u003cp\u003e15.7.7 Smart Retail 338\u003c\/p\u003e \u003cp\u003e15.7.8 Energy Engagement 339\u003c\/p\u003e \u003cp\u003e15.7.9 IoT in Healthcare 340\u003c\/p\u003e \u003cp\u003e15.7.10 IoT in Poultry and Farming 340\u003c\/p\u003e \u003cp\u003e15.8 Deep Learning Applications in the Field of IoT Systems 341\u003c\/p\u003e \u003cp\u003e15.8.1 Organization of DL Applications for IoT in Healthcare 342\u003c\/p\u003e \u003cp\u003e15.8.2 DeepSense as a Solution for Diverse IoT Applications 343\u003c\/p\u003e \u003cp\u003e15.8.3 Deep IoT as a Solution for Energy Efficiency 346\u003c\/p\u003e \u003cp\u003e15.9 Conclusion 346\u003c\/p\u003e \u003cp\u003eReferences 347\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Efficient Detection of Bioweapons for Agricultural Sector Using Narrowband Transmitter and Composite Sensing Architecture 349\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArghyadeep Nag, Labani Roy, Shruti, Soumen Santra and Arpan Deyasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 350\u003c\/p\u003e \u003cp\u003e16.2 Literature Review 353\u003c\/p\u003e \u003cp\u003e16.3 Properties of Insects 355\u003c\/p\u003e \u003cp\u003e16.4 Working Methodology 357\u003c\/p\u003e \u003cp\u003e16.4.1 Sensing 357\u003c\/p\u003e \u003cp\u003e16.4.1.1 Specific Characterization of a Particular Species 357\u003c\/p\u003e \u003cp\u003e16.4.2 Alternative Way to Find Those Previously Sensing Parameters 357\u003c\/p\u003e \u003cp\u003e16.4.3 Remedy to Overcome These Difficulties 358\u003c\/p\u003e \u003cp\u003e16.4.4 Take Necessary Preventive Actions 358\u003c\/p\u003e \u003cp\u003e16.5 Proposed Algorithm 359\u003c\/p\u003e \u003cp\u003e16.6 Block Diagram and Used Sensors 360\u003c\/p\u003e \u003cp\u003e16.6.1 Arduino Uno 361\u003c\/p\u003e \u003cp\u003e16.6.2 Infrared Motion Sensor 362\u003c\/p\u003e \u003cp\u003e16.6.3 Thermographic Camera 362\u003c\/p\u003e \u003cp\u003e16.6.4 Relay Module 362\u003c\/p\u003e \u003cp\u003e16.7 Result Analysis 362\u003c\/p\u003e \u003cp\u003e16.8 Conclusion 363\u003c\/p\u003e \u003cp\u003eReferences 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 A Deep Learning–Based Malware and Intrusion Detection Framework 367\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePavitra Kadiyala and Kakelli Anil Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 367\u003c\/p\u003e \u003cp\u003e17.2 Literature Survey 368\u003c\/p\u003e \u003cp\u003e17.3 Overview of the Proposed Work 371\u003c\/p\u003e \u003cp\u003e17.3.1 Problem Description 371\u003c\/p\u003e \u003cp\u003e17.3.2 The Working Models 371\u003c\/p\u003e \u003cp\u003e17.3.3 About the Dataset 371\u003c\/p\u003e \u003cp\u003e17.3.4 About the Algorithms 373\u003c\/p\u003e \u003cp\u003e17.4 Implementation 374\u003c\/p\u003e \u003cp\u003e17.4.1 Libraries 374\u003c\/p\u003e \u003cp\u003e17.4.2 Algorithm 376\u003c\/p\u003e \u003cp\u003e17.5 Results 376\u003c\/p\u003e \u003cp\u003e17.5.1 Neural Network Models 377\u003c\/p\u003e \u003cp\u003e17.5.2 Accuracy 377\u003c\/p\u003e \u003cp\u003e17.5.3 Web Frameworks 377\u003c\/p\u003e \u003cp\u003e17.6 Conclusion and Future Work 379\u003c\/p\u003e \u003cp\u003eReferences 380\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Phishing URL Detection Based on Deep Learning Techniques 381\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Carolin Jeeva and W. Regis Anne\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 382\u003c\/p\u003e \u003cp\u003e18.1.1 Phishing Life Cycle 382\u003c\/p\u003e \u003cp\u003e18.1.1.1 Planning 383\u003c\/p\u003e \u003cp\u003e18.1.1.2 Collection 384\u003c\/p\u003e \u003cp\u003e18.1.1.3 Fraud 384\u003c\/p\u003e \u003cp\u003e18.2 Literature Survey 385\u003c\/p\u003e \u003cp\u003e18.3 Feature Generation 388\u003c\/p\u003e \u003cp\u003e18.4 Convolutional Neural Network for Classification of Phishing vs Legitimate URLs 388\u003c\/p\u003e \u003cp\u003e18.5 Results and Discussion 391\u003c\/p\u003e \u003cp\u003e18.6 Conclusion 394\u003c\/p\u003e \u003cp\u003eReferences 394\u003c\/p\u003e \u003cp\u003eWeb Citation 396\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Cyber Physical Systems 397\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Cyber Physical System—The Gen Z 399\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJayanta Aich and Mst Rumana Sultana\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 399\u003c\/p\u003e \u003cp\u003e19.2 Architecture and Design 400\u003c\/p\u003e \u003cp\u003e19.2.1 Cyber Family 401\u003c\/p\u003e \u003cp\u003e19.2.2 Physical Family 401\u003c\/p\u003e \u003cp\u003e19.2.3 Cyber-Physical Interface Family 402\u003c\/p\u003e \u003cp\u003e19.3 Distribution and Reliability Management in CPS 403\u003c\/p\u003e \u003cp\u003e19.3.1 CPS Components 403\u003c\/p\u003e \u003cp\u003e19.3.2 CPS Models 404\u003c\/p\u003e \u003cp\u003e19.4 Security Issues in CPS 405\u003c\/p\u003e \u003cp\u003e19.4.1 Cyber Threats 405\u003c\/p\u003e \u003cp\u003e19.4.2 Physical Threats 407\u003c\/p\u003e \u003cp\u003e19.5 Role of Machine Learning in the Field of CPS 408\u003c\/p\u003e \u003cp\u003e19.6 Application 411\u003c\/p\u003e \u003cp\u003e19.7 Conclusion 411\u003c\/p\u003e \u003cp\u003eReferences 411\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 An Overview of Cyber Physical System (CPS) Security, Threats, and Solutions 415\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKrishna Keerthi Chennam, Fahmina Taranum and Maniza Hijab\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 416\u003c\/p\u003e \u003cp\u003e20.1.1 Motivation of Work 417\u003c\/p\u003e \u003cp\u003e20.1.2 Organization of Sections 417\u003c\/p\u003e \u003cp\u003e20.2 Characteristics of CPS 418\u003c\/p\u003e \u003cp\u003e20.3 Types of CPS Security 419\u003c\/p\u003e \u003cp\u003e20.4 Cyber Physical System Security Mechanism—Main Aspects 421\u003c\/p\u003e \u003cp\u003e20.4.1 CPS Security Threats 423\u003c\/p\u003e \u003cp\u003e20.4.2 Information Layer 423\u003c\/p\u003e \u003cp\u003e20.4.3 Perceptual Layer 424\u003c\/p\u003e \u003cp\u003e20.4.4 Application Threats 424\u003c\/p\u003e \u003cp\u003e20.4.5 Infrastructure 425\u003c\/p\u003e \u003cp\u003e20.5 Issues and How to Overcome Them 426\u003c\/p\u003e \u003cp\u003e20.6 Discussion and Solutions 427\u003c\/p\u003e \u003cp\u003e20.7 Conclusion 431\u003c\/p\u003e \u003cp\u003eReferences 431\u003c\/p\u003e \u003cp\u003eIndex 435\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430980055320,"sku":"9781119857211","price":127.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119857211.jpg?v=1784767532","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/convergence-of-deep-learning-in-cyber-iot-systems-and-security-hardback-9781119857211","provider":"Freshly Printed Books","version":"1.0","type":"link"}