Convergence of Deep Learning in Cyber-IoT Systems and Security
- Length: 480 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2022-12-08
- ISBN-10: 111985721X
- ISBN-13: 9781119857211
- Sales Rank: #0 (See Top 100 Books)
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.
Cover Series Page Title Page Copyright Page Preface Part I: VARIOUS APPROACHES FROM MACHINE LEARNING TO DEEP LEARNING 1 Web-Assisted Noninvasive Detection of Oral Submucous Fibrosis Using IoHT 1.1 Introduction 1.2 Literature Survey 1.3 Primary Concepts 1.4 Propose Model 1.5 Comparative Study 1.6 Conclusion References 2 Performance Evaluation of Machine Learning and Deep Learning Techniques: A Comparative Analysis for House Price Prediction 2.1 Introduction 2.2 Related Research 2.3 Research Methodology 2.4 Experimentation 2.5 Results and Discussion 2.6 Suggestions 2.7 Conclusion References 3 Cyber Physical Systems, Machine Learning & Deep Learning—Emergence as an Academic Program and Field for Developing Digital Society 3.1 Introduction 3.2 Objective of the Work 3.3 Methods 3.4 Cyber Physical Systems: Overview with Emerging Academic Potentiality 3.5 ML and DL Basics with Educational Potentialities 3.6 Manpower and Developing Scenario in Machine Learning and Deep Learning 3.7 DL & ML in Indian Context 3.8 Conclusion References 4 Detection of Fake News and Rumors in Social Media Using Machine Learning Techniques With Semantic Attributes 4.1 Introduction 4.2 Literature Survey 4.3 Proposed Work 4.4 Results and Analysis 4.5 Conclusion References Part II: INNOVATIVE SOLUTIONS BASED ON DEEP LEARNING 5 Online Assessment System Using Natural Language Processing Techniques 5.1 Introduction 5.2 Literature Survey 5.3 Existing Algorithms 5.4 Proposed System Design 5.5 System Implementation 5.6 Conclusion References 6 On a Reference Architecture to Build Deep-Q Learning-Based Intelligent IoT Edge Solutions 6.1 Introduction 6.2 Dynamic Programming 6.3 Deep Q-Learning 6.4 IoT 6.5 Conclusion 6.6 Future Work References 7 Fuzzy Logic-Based Air Conditioner System 7.1 Introduction 7.2 Fuzzy Logic-Based Control System 7.3 Proposed System 7.4 Simulated Result 7.5 Conclusion and Future Work References 8 An Efficient Masked-Face Recognition Technique to Combat with COVID-19 8.1 Introduction 8.2 Related Works 8.3 Mathematical Preliminaries 8.4 Proposed Method 8.5 Experimental Results 8.6 Conclusion References 9 Deep Learning: An Approach to Encounter Pandemic Effect of Novel Corona Virus (COVID-19) 9.1 Introduction 9.2 Interpretation With Medical Imaging 9.3 Corona Virus Variants Tracing 9.4 Spreading Capability and Destructiveness of Virus 9.5 Deduction of Biological Protein Structure 9.6 Pandemic Model Structuring and Recommended Drugs 9.7 Selection of Medicine 9.8 Result Analysis 9.9 Conclusion References 10 Question Answering System Using Deep Learning in the Low Resource Language Bengali 10.1 Introduction 10.2 Related Work 10.3 Problem Statement 10.4 Proposed Approach 10.5 Algorithm 10.6 Results and Discussion 10.7 Analysis of Error 10.8 Few Close Observations 10.9 Applications 10.10 Scope for Improvements 10.11 Conclusions Acknowledgments References Part III: SECURITY AND SAFETY ASPECTS WITH DEEP LEARNING 11 Secure Access to Smart Homes Using Biometric Authentication With RFID Reader for IoT Systems 11.1 Introduction 11.2 Related Work 11.3 Framework for Smart Home Use Case With Biometric 11.4 Control Scheme for Secure Access (CSFSC) 11.5 Results Observed Based on Various Features With Proposed and Existing Methods 11.6 Conclusions and Future Work References 12 MQTT-Based Implementation of Home Automation System Prototype With Integrated Cyber-IoT Infrastructure and Deep Learning–Based Security Issues 12.1 Introduction 12.2 Architecture of Implemented Home Automation 12.3 Challenges in Home Automation 12.4 Implementation 12.5 Results and Discussions 12.6 Conclusion References 13 Malware Detection in Deep Learning 13.1 Introduction to Malware 13.2 Machine Learning and Deep Learning for Malware Detection 13.3 Case Study on Malware Detection 13.4 Conclusion References 14 Patron for Women: An Application for Womens Safety 14.1 Introduction 14.2 Background Study 14.3 Related Research 14.4 Proposed Methodology 14.5 Results and Analysis 14.6 Conclusion and Future Work References 15 Concepts and Techniques in Deep Learning Applications in the Field of IoT Systems and Security 15.1 Introduction 15.2 Concepts of Deep Learning 15.3 Techniques of Deep Learning 15.4 Deep Learning Applications 15.5 Concepts of IoT Systems 15.6 Techniques of IoT Systems 15.7 IoT Systems Applications 15.8 Deep Learning Applications in the Field of IoT Systems 15.9 Conclusion References 16 Efficient Detection of Bioweapons for Agricultural Sector Using Narrowband Transmitter and Composite Sensing Architecture 16.1 Introduction 16.2 Literature Review 16.3 Properties of Insects 16.4 Working Methodology 16.5 Proposed Algorithm 16.6 Block Diagram and Used Sensors 16.7 Result Analysis 16.8 Conclusion References 17 A Deep Learning–Based Malware and Intrusion Detection Framework 17.1 Introduction 17.2 Literature Survey 17.3 Overview of the Proposed Work 17.4 Implementation 17.5 Results 17.6 Conclusion and Future Work References 18 Phishing URL Detection Based on Deep Learning Techniques 18.1 Introduction 18.2 Literature Survey 18.3 Feature Generation 18.4 Convolutional Neural Network for Classification of Phishing vs Legitimate URLs 18.5 Results and Discussion 18.6 Conclusion References Web Citation Part IV: CYBER PHYSICAL SYSTEMS 19 Cyber Physical System—The Gen Z 19.1 Introduction 19.2 Architecture and Design 19.3 Distribution and Reliability Management in CPS 19.4 Security Issues in CPS 19.5 Role of Machine Learning in the Field of CPS 19.6 Application 19.7 Conclusion References 20 An Overview of Cyber Physical System (CPS) Security, Threats, and Solutions 20.1 Introduction 20.2 Characteristics of CPS 20.3 Types of CPS Security 20.4 Cyber Physical System Security Mechanism— Main Aspects 20.5 Issues and How to Overcome Them 20.6 Discussion and Solutions 20.7 Conclusion References Index Also of Interest End User License Agreement
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