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E-grāmata: Smart Cyber Ecosystem for Sustainable Development

Edited by (Quaid-e-Awam University of Engineering, Science & Technology (QUEST) Nawabshah, Pakistan), Edited by (Universiti Tunku Abdul Rahman, Malaysia), Edited by (Sharda University, Greater Noida, U.P. India)
  • Formāts: PDF+DRM
  • Izdošanas datums: 27-Aug-2021
  • Izdevniecība: Wiley-Scrivener
  • Valoda: eng
  • ISBN-13: 9781119761679
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Smart Cyber Ecosystem for Sustainable DevelopmentPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 27-Aug-2021
  • Izdevniecība: Wiley-Scrivener
  • Valoda: eng
  • ISBN-13: 9781119761679
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The Smart Cyber Ecosystem for Sustainable Development

As the entire ecosystem is moving towards a sustainable goal, technology driven smart cyber system is the enabling factor to make this a success, and the current book documents how this can be attained.

The cyber ecosystem consists of a huge number of different entities that work and interact with each other in a highly diversified manner. In this era, when the world is surrounded by many unseen challenges and when its population is increasing and resources are decreasing, scientists, researchers, academicians, industrialists, government agencies and other stakeholders are looking toward smart and intelligent cyber systems that can guarantee sustainable development for a better and healthier ecosystem. The main actors of this cyber ecosystem include the Internet of Things (IoT), artificial intelligence (AI), and the mechanisms providing cybersecurity.

This book attempts to collect and publish innovative ideas, emerging trends, implementation experiences, and pertinent user cases for the purpose of serving mankind and societies with sustainable societal development. The 22 chapters of the book are divided into three sections: Section I deals with the Internet of Things, Section II focuses on artificial intelligence and especially its applications in healthcare, whereas Section III investigates the different cyber security mechanisms.

Audience

This book will attract researchers and graduate students working in the areas of artificial intelligence, blockchain, Internet of Things, information technology, as well as industrialists, practitioners, technology developers, entrepreneurs, and professionals who are interested in exploring, designing and implementing these technologies.

Preface xxi
Part 1 Internet of Things
1(214)
1 Voyage of Internet of Things in the Ocean of Technology
3(22)
Tejaskumar R. Ghadiyali
Bharat C. Patel
Manish M. Kayasth
1.1 Introduction
3(4)
1.1.1 Characteristics of IoT
4(1)
1.1.2 IoT Architecture
5(1)
1.1.3 Merits and Demerits of IoT
6(1)
1.2 Technological Evolution Toward IoT
7(1)
1.3 IoT-Associated Technology
8(6)
1.4 Interoperability in IoT
14(1)
1.5 Programming Technologies in IoT
15(4)
1.5.1 Arduino
15(2)
1.5.2 Raspberry Pi
17(1)
1.5.3 Python
18(1)
1.6 IoT Applications
19(6)
Conclusion
22(1)
References
22(3)
2 AI for Wireless Network Optimization: Challenges and Opportunities
25(32)
Murad Abusubaih
2.1 Introduction to AI
25(2)
2.2 Self-Organizing Networks
27(2)
2.2.1 Operation Principle of Self-Organizing Networks
27(1)
2.2.2 Self-Configuration
28(1)
2.2.3 Self-Optimization
28(1)
2.2.4 Self-Healing
28(1)
2.2.5 Key Performance Indicators
29(1)
2.2.6 SON Functions
29(1)
2.3 Cognitive Networks
29(1)
2.4 Introduction to Machine Learning
30(6)
2.4.1 ML Types
31(1)
2.4.2 Components of ML Algorithms
31(1)
2.4.3 How do Machines Learn?
32(1)
2.4.3.1 Supervised Learning
32(1)
2.4.3.2 Unsupervised Learning
33(2)
2.4.3.3 Semi-Supervised Learning
35(1)
2.4.3.4 Reinforcement Learning
35(1)
2.4.4 ML and Wireless Networks
36(1)
2.5 Software-Defined Networks
36(3)
2.5.1 SDN Architecture
37(1)
2.5.2 The OpenFlow Protocol
38(1)
2.5.3 SDN and ML
39(1)
2.6 Cognitive Radio Networks
39(2)
2.6.1 Sensing Methods
41(1)
2.7 ML for Wireless Networks: Challenges and Solution Approaches
41(16)
2.7.1 Cellular Networks
42(1)
2.7.1.1 Energy Saving
42(1)
2.7.1.2 Channel Access and Assignment
42(1)
2.7.1.3 User Association and Load Balancing
43(1)
2.7.1.4 Traffic Engineering
44(1)
2.7.1.5 QoS/QoE Prediction
45(1)
2.7.1.6 Security
45(1)
2.7.2 Wireless Local Area Networks
46(1)
2.7.2.1 Access Point Selection
47(1)
2.7.2.2 Interference Mitigation
48(1)
2.7.2.3 Channel Allocation and Channel Bonding
49(1)
2.7.2.4 Latency Estimation and Frame Length Selection
49(1)
2.7.2.5 Handover
49(1)
2.7.3 Cognitive Radio Networks
50(1)
References
50(7)
3 An Overview on Internet of Things (IoT) Segments and Technologies
57(12)
Amarjit Singh
3.1 Introduction
57(2)
3.2 Features of IoT
59(1)
3.3 IoT Sensor Devices
59(2)
3.4 IoT Architecture
61(1)
3.5 Challenges and Issues in IoT
62(1)
3.6 Future Opportunities in IoT
63(1)
3.7 Discussion
64(1)
3.8 Conclusion
65(4)
References
65(4)
4 The Technological Shift: AI in Big Data and IoT
69(22)
Deepti Sharma
Amandeep Singh
Sanyam Singhal
4.1 Introduction
69(2)
4.2 Artificial Intelligence
71(4)
4.2.1 Machine Learning
71(2)
4.2.2 Further Development in the Domain of Artificial Intelligence
73(1)
4.2.3 Programming Languages for Artificial Intelligence
74(1)
4.2.4 Outcomes of Artificial Intelligence
74(1)
4.3 Big Data
75(5)
4.3.1 Artificial Intelligence Methods for Big Data
77(1)
4.3.2 Industry Perspective of Big Data
77(1)
4.3.2.1 In Medical Field
78(1)
4.3.2.2 In Meteorological Department
78(1)
4.3.2.3 In Industrial/Corporate Applications and Analytics
79(1)
4.3.2.4 In Education
79(1)
4.3.2.5 In Astronomy
79(1)
4.4 Internet of Things
80(2)
4.4.1 Interconnection of IoT With AoT
81(1)
4.4.2 Difference Between IIoT and IoT
81(1)
4.4.3 Industrial Approach for IoT
82(1)
4.5 Technical Shift in AI, Big Data, and IoT
82(3)
4.5.1 Industries Shifting to AI-Enabled Big Data Analytics
83(1)
4.5.2 Industries Shifting to Al-Powered IoT Devices
84(1)
4.5.3 Statistical Data of These Shifts
84(1)
4.6 Conclusion
85(6)
References
86(5)
5 IoT's Data Processing Using Spark
91(20)
Ankita Bansal
Aditya Atri
5.1 Introduction
91(1)
5.2 Introduction to Apache Spark
92(2)
5.2.1 Advantages of Apache Spark
93(1)
5.2.2 Apache Spark's Components
93(1)
5.3 Apache Hadoop MapReduce
94(1)
5.3.1 Limitations of MapReduce
94(1)
5.4 Resilient Distributed Dataset (RDD)
95(1)
5.4.1 Features and Limitations of RDDs
95(1)
5.5 DataFrames
96(1)
5.6 Datasets
97(1)
5.7 Introduction to Spark SQL
98(2)
5.7.1 Spark SQL Architecture
99(1)
5.7.2 Spark SQL Libraries
100(1)
5.8 SQL Context Class in Spark
100(1)
5.9 Creating Dataframes
101(2)
5.9.1 Operations on DataFrames
102(1)
5.10 Aggregations
103(1)
5.11 Running SQL Queries on Dataframes
103(1)
5.12 Integration With RDDs
104(1)
5.12.1 Inferring the Schema Using Reflection
104(1)
5.12.2 Specifying the Schema Programmatically
104(1)
5.13 Data Sources
104(2)
5.13.1 JSON Datasets
105(1)
5.13.2 Hive Tables
105(1)
5.13.3 Parquet Files
106(1)
5.14 Operations on Data Sources
106(1)
5.15 Industrial Applications
107(1)
5.16 Conclusion
108(3)
References
108(3)
6 SE-TEM: Simple and Efficient Trust Evaluation Model for WSNs
111(20)
Tayyab Khan
Karan Singh
6.1 Introduction
111(10)
6.1.1 Components of WSNs
113(2)
6.1.2 Trust
115(5)
6.1.3 Major Contribution
120(1)
6.2 Related Work
121(1)
6.3 Network Topology and Assumptions
122(1)
6.4 Proposed Trust Model
122(4)
6.4.1 CM to CM (Direct) Trust Evaluation Scheme
123(1)
6.4.2 CM to CM Peer Recommendation (Indirect) Trust Estimation (PRx, y(Δt))
124(1)
6.4.3 CH-to-CH Direct Trust Estimation
125(1)
6.4.4 BS-to-CH Feedback Trust Calculation
125(1)
6.5 Result and Analysis
126(2)
6.5.1 Severity Analysis
126(1)
6.5.2 Malicious Node Detection
127(1)
6.6 Conclusion and Future Work
128(3)
References
128(3)
7 Smart Applications of IoT
131(22)
Pradeep Kamboj
T. Ratha Jeyalakshmi
P. Thillai Arasu
S. Balamurali
A. Murugan
7.1 Introduction
131(1)
7.2 Background
132(4)
7.2.1 Enabling Technologies for Building Intelligent Infrastructure
132(4)
7.3 Smart City
136(3)
7.3.1 Benefits of a Smart City
137(1)
7.3.2 Smart City Ecosystem
137(1)
7.3.3 Challenges in Smart Cities
138(1)
7.4 Smart Healthcare
139(3)
7.4.1 Smart Healthcare Applications
140(1)
7.4.2 Challenges in Healthcare
141(1)
7.5 Smart Agriculture
142(3)
7.5.1 Environment Agriculture Controlling
143(1)
7.5.2 Advantages
143(1)
7.5.3 Challenges
144(1)
7.6 Smart Industries
145(4)
7.6.1 Advantages
147(1)
7.6.2 Challenges
148(1)
7.7 Future Research Directions
149(1)
7.8 Conclusions
149(4)
References
149(4)
8 Sensor-Based Irrigation System: Introducing Technology in Agriculture
153(14)
Rohit Rastogi
Krishna Vir Singh
Mihir Rai
Kartik Sachdeva
Tarun Yadav
Harshit Gupta
8.1 Introduction
153(1)
8.1.1 Technology in Agriculture
154(1)
8.1.2 Use and Need for Low-Cost Technology in Agriculture
154(1)
8.2 Proposed System
154(3)
8.3 Flowchart
157(1)
8.4 Use Case
158(1)
8.5 System Modules
158(4)
8.5.1 Raspberry Pi
158(1)
8.5.2 Arduino Uno
158(1)
8.5.3 DHT 11 Humidity and Temperature Sensor
158(2)
8.5.4 Soil Moisture Sensor
160(1)
8.5.5 Solenoid Valve
160(1)
8.5.6 Drip Irrigation Kit
160(1)
8.5.7 433 MHz RF Module
160(1)
8.5.8 Mobile Application
160(1)
8.5.9 Testing Phase
161(1)
8.6 Limitations
162(1)
8.7 Suggestions
162(1)
8.8 Future Scope
162(1)
8.9 Conclusion
163(4)
Acknowledgement
163(1)
References
163(1)
Suggested Additional Readings
164(1)
Key Terms and Definitions
164(1)
Appendix
165(1)
Example Code
166(1)
9 Artificial Intelligence: An Imaginary World of Machine
167(18)
Bharat C. Patel
Manish M. Kaysth
Tejaskumar R. Ghadiyali
9.1 The Dawn of Artificial Intelligence
167(2)
9.2 Introduction
169(1)
9.3 Components of AI
170(2)
9.3.1 Machine Reasoning
170(1)
9.3.2 Natural Language Processing
171(1)
9.3.3 Automated Planning
171(1)
9.3.4 Machine Learning
171(1)
9.4 Types of Artificial Intelligence
172(3)
9.4.1 Artificial Narrow Intelligence
172(1)
9.4.2 Artificial General Intelligence
173(1)
9.4.3 Artificial Super Intelligence
174(1)
9.5 Application Area of AI
175(1)
9.6 Challenges in Artificial Intelligence
176(1)
9.7 Future Trends in Artificial Intelligence
177(2)
9.8 Practical Implementation of AI Application
179(6)
References
182(3)
10 Impact of Deep Learning Techniques in IoT
185(30)
M. Chandra Vadhana
P. Shanthi Bala
Immanuel Zion Ramdinthara
10.1 Introduction
185(1)
10.2 Internet of Things
186(12)
10.2.1 Characteristics of IoT
187(1)
10.2.2 Architecture of IoT
187(1)
10.2.2.1 Smart Device/Sensor Layer
187(1)
10.2.2.2 Gateways and Networks
187(1)
10.2.2.3 Management Service Layer
188(1)
10.2.2.4 Application Layer
188(1)
10.2.2.5 Interoperability of IoT
188(2)
10.2.2.6 Security Requirements at a Different Layer of IoT
190(1)
10.2.2.7 Future Challenges for IoT
190(1)
10.2.2.8 Privacy and Security
190(1)
10.2.2.9 Cost and Usability
191(1)
10.2.2.10 Data Management
191(1)
10.2.2.11 Energy Preservation
191(1)
10.2.2.12 Applications of IoT
191(2)
10.2.2.13 Essential IoT Technologies
193(2)
10.2.2.14 Enriching the Customer Value
195(1)
10.2.2.15 Evolution of the Foundational IoT Technologies
196(1)
10.2.2.16 Technical Challenges in the IoT Environment
196(1)
10.2.2.17 Security Challenge
197(1)
10.2.2.18 Chaos Challenge
197(1)
10.2.2.19 Advantages of IoT
198(1)
10.2.2.20 Disadvantages of IoT
198(1)
10.3 Deep Learning
198(8)
10.3.1 Models of Deep Learning
199(1)
10.3.1.1 Convolutional Neural Network
199(1)
10.3.1.2 Recurrent Neural Networks
199(1)
10.3.1.3 Long Short-Term Memory
200(1)
10.3.1.4 Autoencoders
200(1)
10.3.1.5 Variational Autoencoders
201(1)
10.3.1.6 Generative Adversarial Networks
201(1)
10.3.1.7 Restricted Boltzmann Machine
201(1)
10.3.1.8 Deep Belief Network
201(1)
10.3.1.9 Ladder Networks
202(1)
10.3.2 Applications of Deep Learning
202(1)
10.3.2.1 Industrial Robotics
202(1)
10.3.2.2 E-Commerce Industries
202(1)
10.3.2.3 Self-Driving Cars
202(1)
10.3.2.4 Voice-Activated Assistants
202(1)
10.3.2.5 Automatic Machine Translation
202(1)
10.3.2.6 Automatic Handwriting Translation
203(1)
10.3.2.7 Predicting Earthquakes
203(1)
10.3.2.8 Object Classification in Photographs
203(1)
10.3.2.9 Automatic Game Playing
203(1)
10.3.2.10 Adding Sound to Silent Movies
203(1)
10.3.3 Advantages of Deep Learning
203(1)
10.3.4 Disadvantages of Deep Learning
203(1)
10.3.5 Deployment of Deep Learning in IoT
203(1)
10.3.6 Deep Learning Applications in IoT
204(1)
10.3.6.1 Image Recognition
204(1)
10.3.6.2 Speech/Voice Recognition
204(1)
10.3.6.3 Indoor Localization
204(1)
10.3.6.4 Physiological and Psychological Detection
205(1)
10.3.6.5 Security and Privacy
205(1)
10.3.7 Deep Learning Techniques on IoT Devices
205(1)
10.3.7.1 Network Compression
205(1)
10.3.7.2 Approximate Computing
206(1)
10.3.7.3 Accelerators
206(1)
10.3.7.4 Tiny Motes
206(1)
10.4 IoT Challenges on Deep Learning and Future Directions
206(1)
10.4.1 Lack of IoT Dataset
206(1)
10.4.2 Pre-Processing
207(1)
10.4.3 Challenges of 6V's
207(1)
10.4.4 Deep Learning Limitations
207(1)
10.5 Future Directions of Deep Learning
207(2)
10.5.1 IoT Mobile Data
207(1)
10.5.2 Integrating Contextual Information
208(1)
10.5.3 Online Resource Provisioning for IoT Analytics
208(1)
10.5.4 Semi-Supervised Analytic Framework
208(1)
10.5.5 Dependable and Reliable IoT Analytics
208(1)
10.5.6 Self-Organizing Communication Networks
208(1)
10.5.7 Emerging IoT Applications
208(1)
10.5.7.1 Unmanned Aerial Vehicles
209(1)
10.5.7.2 Virtual/Augmented Reality
209(1)
10.5.7.3 Mobile Robotics
209(1)
10.6 Common Datasets for Deep Learning in IoT
209(1)
10.7 Discussion
209(2)
10.8 Conclusion
211(4)
References
211(4)
Part 2 Artificial Intelligence in Healthcare
215(108)
11 Non-Invasive Process for Analyzing Retinal Blood Vessels Using Deep Learning Techniques
217(18)
Toufique A. Soomro
Ahmed J. Afifi
Pardeep Kumar
Muhammad Usman Keerio
Saleem Ahmed
Ahmed Ali
11.1 Introduction
217(4)
11.2 Existing Methods Review
221(2)
11.3 Methodology
223(2)
11.3.1 Architecture of Stride U-Net
223(2)
11.3.2 Loss Function
225(1)
11.4 Databases and Evaluation Metrics
225(2)
11.4.1 CNN Implementation Details
226(1)
11.5 Results and Analysis
227(2)
11.5.1 Evaluation on DRIVE and STARE Databases
227(1)
11.5.2 Comparative Analysis
227(2)
11.6 Concluding Remarks
229(6)
References
230(5)
12 Existing Trends in Mental Health Based on IoT Applications: A Systematic Review
235(16)
Muhammad Ali Nizamani
Muhammad Ali Memon
Pirah Brohi
12.1 Introduction
235(2)
12.2 Methodology
237(1)
12.3 IoT in Mental Health
238(1)
12.4 Mental Healthcare Applications and Services Based on IoT
238(3)
12.5 Benefits of IoT in Mental Health
241(1)
12.5.1 Reduction in Treatment Cost
241(1)
12.5.2 Reduce Human Error
241(1)
12.5.3 Remove Geographical Barriers
241(1)
12.5.4 Less Paperwork and Documentation
241(1)
12.5.5 Early Stage Detection of Chronic Disorders
241(1)
12.5.6 Improved Drug Management
242(1)
12.5.7 Speedy Medical Attention
242(1)
12.5.8 Reliable Results of Treatment
242(1)
12.6 Challenges in IoT-Based Mental Healthcare Applications
242(3)
12.6.1 Scalability
242(1)
12.6.2 Trust
242(1)
12.6.3 Security and Privacy Issues
243(1)
12.6.4 Interoperability Issues
243(1)
12.6.5 Computational Limits
243(1)
12.6.6 Memory Limitations
243(1)
12.6.7 Communications Media
244(1)
12.6.8 Devices Multiplicity
244(1)
12.6.9 Standardization
244(1)
12.6.10 IoT-Based Healthcare Platforms
244(1)
12.6.11 Network Type
244(1)
12.6.12 Quality of Service
245(1)
12.7 Blockchain in IoT for Healthcare
245(1)
12.8 Results and Discussion
246(1)
12.9 Limitations of the Survey
247(1)
12.10 Conclusion
247(4)
References
247(4)
13 Monitoring Technologies for Precision Health
251(10)
Rehab A. Rayan
Imran Zafar
13.1 Introduction
251(1)
13.2 Applications of Monitoring Technologies
252(3)
13.2.1 Everyday Life Activities
253(1)
13.2.2 Sleeping and Stress
253(1)
13.2.3 Breathing Patterns and Respiration
254(1)
13.2.4 Energy and Caloric Consumption
254(1)
13.2.5 Diabetes, Cardiac, and Cognitive Care
254(1)
13.2.6 Disability and Rehabilitation
254(1)
13.2.7 Pregnancy and Post-Procedural Care
255(1)
13.3 Limitations
255(1)
13.3.1 Quality of Data and Reliability
255(1)
13.3.2 Safety, Privacy, and Legal Concerns
256(1)
13.4 Future Insights
256(1)
13.4.1 Consolidating Frameworks
256(1)
13.4.2 Monitoring and Intervention
256(1)
13.4.3 Research and Development
257(1)
13.5 Conclusions
257(4)
References
257(4)
14 Impact of Artificial Intelligence in Cardiovascular Disease
261(12)
Mir Khan
Saleem Ahmed
Pardeep Kumar
Dost Muhammad Saqib Bhatti
14.1 Artificial Intelligence
261(1)
14.2 Machine Learning
262(1)
14.3 The Application of AI in CVD
263(1)
14.3.1 Precision Medicine
263(1)
14.3.2 Clinical Prediction
263(1)
14.3.3 Cardiac Imaging Analysis
264(1)
14.4 Future Prospect
264(1)
14.5 PUAI and Novel Medical Mode
265(1)
14.5.1 Phenomenon of PUAI
265(1)
14.5.2 Novel Medical Model
266(1)
14.6 Traditional Mode
266(2)
14.6.1 Novel Medical Mode Plus PUAI
266(2)
14.7 Representative Calculations of AI
268(1)
14.8 Overview of Pipeline for Image-Based Machine Learning Diagnosis
268(5)
References
270(3)
15 Healthcare Transformation With Clinical Big Data Predictive Analytics
273(14)
Muhammad Suleman Memon
Pardeep Kumar
Azeem Ayaz Mirani
Mumtaz Qabulio
Sumera Naz Pathan
Asia Khatoon Soomro
15.1 Introduction
273(3)
15.1.1 Big Data in Health Sector
275(1)
15.1.2 Data Structure Produced in Health Sectors
275(1)
15.2 Big Data Challenges in Healthcare
276(2)
15.2.1 Big Data in Computational Healthcare
276(1)
15.2.2 Big Data Predictive Analytics in Healthcare
276(1)
15.2.3 Big Data for Adapted Healthcare
277(1)
15.3 Cloud Computing and Big Data in Healthcare
278(1)
15.4 Big Data Healthcare and IoT
278(4)
15.5 Wearable Devices for Patient Health Monitoring
282(1)
15.6 Big Data and Industry 4.0
283(1)
15.7 Conclusion
283(4)
References
284(3)
16 Computing Analysis of Yajna and Mantra Chanting as a Therapy: A Holistic Approach for All by Indian Continent Amidst Pandemic Threats
287(20)
Rohit Rastogi
Mamta Saxena
D.K. Chaturvedi
Mayank Gupta
Mukund Rastogi
Prajwal Srivatava
Mohit Jain
Pradeep Kumar
Ujjawal Sharma
Rohan Choudhary
Neha Gupta
16.1 Introduction
287(3)
16.1.1 The Stats of Different Diseases, Comparative Observation on Symptoms, and Mortality Rate
287(1)
16.1.2 Precautionary Guidelines Followed in Indian Continent
288(1)
16.1.3 Spiritual Guidelines in Indian Society
289(1)
16.1.3.1 Spiritual Defense Against Global Corona by Swami Bhoomananda Tirtha of Trichura, Kerala, India
289(1)
16.1.4 Veda Vigyaan: Ancient Vedic Knowledge
289(1)
16.1.5 Yagyopathy Researches, Say, Smoke of Yagya is Boon
289(1)
16.1.6 The Yagya Samagri
290(1)
16.2 Literature Survey
290(2)
16.2.1 Technical Aspects of Yajna and Mantra Therapy
290(1)
16.2.2 Mantra Chanting and Its Science
290(1)
16.2.3 Yagya Medicine (Yagyopathy)
290(1)
16.2.4 The Medicinal HavanSamagri Components
291(1)
16.2.4.1 Special Havan Ingredients to Fight Against Infectious Diseases
291(1)
16.2.5 Scientific Benefits of Havan
291(1)
16.3 Experimental Setup Protocols With Results
292(5)
16.3.1 Subject Sample Distribution
295(1)
16.3.1.1 Area Wise Distribution
295(1)
16.3.2 Conclusion and Discussion Through Experimental Work
295(2)
16.4 Future Scope and Limitations
297(1)
16.5 Novelty
298(1)
16.6 Recommendations
298(1)
16.7 Applications of Yajna Therapy
299(1)
16.8 Conclusions
299(8)
Acknowledgement
299(1)
References
299(5)
Key Terms and Definitions
304(3)
17 Extraction of Depression Symptoms From Social Networks
307(16)
Bhavna Chilwal
Amit Kumar Mishra
17.1 Introduction
307(3)
17.1.1 Diagnosis and Treatments
309(1)
17.2 Data Mining in Healthcare
310(1)
17.2.1 Text Mining
310(1)
17.3 Social Network Sites
311(1)
17.4 Symptom Extraction Tool
312(4)
17.4.1 Data Collection
313(1)
17.4.2 Data Processing
313(1)
17.4.3 Data Analysis
314(2)
17.5 Sentiment Analysis
316(3)
17.5.1 Emotion Analysis
318(1)
17.5.2 Behavioral Analysis
318(1)
17.6 Conclusion
319(4)
References
320(3)
Part 3 Cybersecurity
323(120)
18 Fog Computing Perspective: Technical Trends, Security Practices, and Recommendations
325(28)
C. Kaviyazhiny
P. Shanthi Bala
A.S. Gowri
18.1 Introduction
325(1)
18.2 Characteristics of Fog Computing
326(2)
18.3 Reference Architecture of Fog Computing
328(1)
18.4 CISCO IOx Framework
329(1)
18.5 Security Practices in CISCO IOx
330(3)
18.5.1 Potential Attacks on IoT Architecture
330(1)
18.5.2 Perception Layer (Sensing)
331(1)
18.5.3 Network Layer
331(1)
18.5.4 Service Layer (Support)
332(1)
18.5.5 Application Layer (Interface)
333(1)
18.6 Security Issues in Fog Computing
333(5)
18.6.1 Virtualization Issues
333(1)
18.6.2 Web Security Issues
334(1)
18.6.3 Internal/External Communication Issues
335(1)
18.6.4 Data Security Related Issues
336(1)
18.6.5 Wireless Security Issues
337(1)
18.6.6 Malware Protection
338(1)
18.7 Machine Learning for Secure Fog Computing
338(3)
18.7.1 Layer 1 Cloud
339(1)
18.7.2 Layer 2 Fog Nodes For The Community
340(1)
18.7.3 Layer 3 Fog Node for Their Neighborhood
340(1)
18.7.4 Layer 4 Sensors
341(1)
18.8 Existing Security Solution in Fog Computing
341(4)
18.8.1 Privacy-Preserving in Fog Computing
341(1)
18.8.2 Pseudocode for Privacy Preserving in Fog Computing
342(1)
18.8.3 Pseudocode for Feature Extraction
343(1)
18.8.4 Pseudocode for Adding Gaussian Noise to the Extracted Feature
343(1)
18.8.5 Pseudocode for Encrypting Data
344(1)
18.8.6 Pseudocode for Data Partitioning
344(1)
18.8.7 Encryption Algorithms in Fog Computing
345(1)
18.9 Recommendation and Future Enhancement
345(4)
18.9.1 Data Encryption
345(1)
18.9.2 Preventing from Cache Attacks
346(1)
18.9.3 Network Monitoring
346(1)
18.9.4 Malware Protection
347(1)
18.9.5 Wireless Security
347(1)
18.9.6 Secured Vehicular Network
347(1)
18.9.7 Secure Multi-Tenancy
348(1)
18.9.8 Backup and Recovery
348(1)
18.9.9 Security with Performance
348(1)
18.10 Conclusion
349(4)
References
349(4)
19 Cybersecurity and Privacy Fundamentals
353(26)
Ravi Verma
19.1 Introduction
353(1)
19.2 Historical Background and Evolution of Cyber Crime
354(1)
19.3 Introduction to Cybersecurity
355(2)
19.3.1 Application Security
356(1)
19.3.2 Information Security
356(1)
19.3.3 Recovery From Failure or Disaster
356(1)
19.3.4 Network Security
357(1)
19.4 Classification of Cyber Crimes
357(1)
19.4.1 Internal Attacks
357(1)
19.4.2 External Attacks
358(1)
19.4.3 Unstructured Attack
358(1)
19.4.4 Structured Attack
358(1)
19.5 Reasons Behind Cyber Crime
358(1)
19.5.1 Making Money
359(1)
19.5.2 Gaining Financial Growth and Reputation
359(1)
19.5.3 Revenge
359(1)
19.5.4 For Making Fun
359(1)
19.5.5 To Recognize
359(1)
19.5.6 Business Analysis and Decision Making
359(1)
19.6 Various Types of Cyber Crime
359(2)
19.6.1 Cyber Stalking
360(1)
19.6.2 Sexual Harassment or Child Pornography
360(1)
19.6.3 Forgery
360(1)
19.6.4 Crime Related to Privacy of Software and Network Resources
360(1)
19.6.5 Cyber Terrorism
360(1)
19.6.6 Phishing, Vishing, and Smishing
360(1)
19.6.7 Malfunction
361(1)
19.6.8 Server Hacking
361(1)
19.6.9 Spreading Virus
361(1)
19.6.10 Spamming, Cross Site Scripting, and Web Jacking
361(1)
19.7 Various Types of Cyber Attacks in Information Security
361(4)
19.7.1 Web-Based Attacks in Information Security
362(2)
19.7.2 System-Based Attacks in Information Security
364(1)
19.8 Cybersecurity and Privacy Techniques
365(5)
19.8.1 Authentication and Authorization
365(1)
19.8.2 Cryptography
366(1)
19.8.2.1 Symmetric Key Encryption
367(1)
19.8.2.2 Asymmetric Key Encryption
367(1)
19.8.3 Installation of Antivirus
367(1)
19.8.4 Digital Signature
367(2)
19.8.5 Firewall
369(1)
19.8.6 Steganography
369(1)
19.9 Essential Elements of Cybersecurity
370(1)
19.10 Basic Security Concerns for Cybersecurity
371(2)
19.10.1 Precaution
372(1)
19.10.2 Maintenance
372(1)
19.10.3 Reactions
373(1)
19.11 Cybersecurity Layered Stack
373(1)
19.12 Basic Security and Privacy Check List
374(1)
19.13 Future Challenges of Cybersecurity
374(5)
References
376(3)
20 Changing the Conventional Banking System through Blockchain
379(26)
Khushboo Tripathi
Neha Bhateja
Ashish Dhillon
20.1 Introduction
379(9)
20.1.1 Introduction to Blockchain
379(2)
20.1.2 Classification of Blockchains
381(1)
20.1.2.1 Public Blockchain
381(1)
20.1.2.2 Private Blockchain
382(1)
20.1.2.3 Hybrid Blockchain
382(1)
20.1.2.4 Consortium Blockchain
382(1)
20.1.3 Need for Blockchain Technology
383(1)
20.1.3.1 Bitcoin vs. Mastercard Transactions: A Summary
383(1)
20.1.4 Comparison of Blockchain and Cryptocurrency
384(1)
20.1.4.1 Distributed Ledger Technology (DLT)
384(1)
20.1.5 Types of Consensus Mechanism
385(1)
20.1.5.1 Consensus Algorithm: A Quick Background
385(1)
20.1.6 Proof of Work
386(1)
20.1.7 Proof of Stake
387(1)
20.1.7.1 Delegated Proof of Stake
387(1)
20.1.7.2 Byzantine Fault Tolerance
388(1)
20.2 Literature Survey
388(4)
20.2.1 The History of Blockchain Technology
388(1)
20.2.2 Early Years of Blockchain Technology: 1991---2008
389(1)
20.2.2.1 Evolution of Blockchain: Phase 1---Transactions
389(1)
20.2.2.2 Evolution of Blockchain: Phase 2---Contracts
390(1)
20.2.2.3 Evolution of Blockchain: Phase 3---Applications
390(1)
20.2.3 Literature Review
391(1)
20.2.4 Analysis
392(1)
20.3 Methodology and Tools
392(2)
20.3.1 Methodology
392(1)
20.3.2 Flow Chart
393(1)
20.3.3 Tools and Configuration
394(1)
20.4 Experiment
394(4)
20.4.1 Steps of Implementation
394(3)
20.4.2 Screenshots of Experiment
397(1)
20.5 Results
398(2)
20.6 Conclusion
400(1)
20.7 Future Scope
401(4)
20.7.1 Blockchain as a Service (BaaS) is Gaining Adoption From Enterprises
401(1)
References
402(3)
21 A Secured Online Voting System by Using Blockchain as the Medium
405(26)
Leslie Mark
Vasaki Ponnusatny
Arya Wicaksana
Basilius Bias Christyono
Moeljono Widjaja
21.1 Blockchain-Based Online Voting System
405(5)
21.1.1 Introduction
405(1)
21.1.2 Structure of a Block in a Blockchain System
406(1)
21.1.3 Function of Segments in a Block of the Blockchain
406(1)
21.1.4 SHA-256 Hashing on the Blockchain
407(2)
21.1.5 Interaction Involved in Blockchain-Based Online Voting System
409(1)
21.1.6 Online Voting System Using Blockchain -- Framework
409(1)
21.2 Literature Review
410(21)
21.2.1 Literature Review Outline
410(1)
21.2.1.1 Online Voting System Based on Cryptographic and Stego-Cryptographic Model
410(1)
21.2.1.2 Online Voting System Based on Visual Cryptography
411(1)
21.2.1.3 Online Voting System Using Biometric Security and Steganography
412(2)
21.2.1.4 Cloud-Based Secured Online Voting System Using Homomorphic Encryption
414(2)
21.2.1.5 An Online Voting System Based on a Secured Blockchain
416(1)
21.2.1.6 Online Voting System Using Fingerprint Biometric and Crypto-Watermarking Approach
417(1)
21.2.1.7 Online Voting System Using Iris Recognition
418(2)
21.2.1.8 Online Voting System Based on NID and SIM
420(2)
21.2.1.9 Online Voting System Using Image Steganography and Visual Cryptography
422(3)
21.2.1.10 Online Voting System Using Secret Sharing-Based Authentication
425(2)
21.2.2 Comparing the Existing Online Voting System
427(3)
References
430(1)
22 Artificial Intelligence and Cybersecurity: Current Trends and Future Prospects
431(12)
Abhinav Juneja
Sapna Juneja
Vikram Bali
Vishal Jain
Hemant Upadhyay
22.1 Introduction
431(1)
22.2 Literature Review
432(1)
22.3 Different Variants of Cybersecurity in Action
432(1)
22.4 Importance of Cybersecurity in Action
433(1)
22.5 Methods for Establishing a Strategy for Cybersecurity
434(1)
22.6 The Influence of Artificial Intelligence in the Domain of Cybersecurity
434(3)
22.7 Where AI Is Actually Required to Deal With Cybersecurity
437(1)
22.8 Challenges for Cybersecurity in Current State of Practice
438(1)
22.9 Conclusion
438(5)
References
438(5)
Index 443
Pardeep Kumar is a Professor and Head of the Software Engineering Department and Director ORIC, Quaid-e-Awam University of Engineering, Science & Technology (QUEST) Nawabshah, Pakistan. He completed his PhD from Berlin, Germany in 2012. He has authored more than 50 research publications in reputed journals and conferences around the world including three books and several book chapters.

Vishal Jain PhD is an associate professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, U.P. India. He has authored more than 85 research papers in reputed conferences and journals, and has authored and edited more than 10 books.

Vasaki Ponnusamy is an assistant professor in the Universiti Tunku Abdul Rahman, Malaysia where she heads the Department of Computer and Communication Technology. She obtained her PhD in IT from Universiti Teknologi PETRONAS (UTP), Malaysia (2013).
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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