Decision Making and Security Risk Management for IoT Environments (Advances in Information Security, 106)

About the Book
Contents
Internet of Things Overview: Architecture, Technologies, Application, and Challenges
	1 Introduction
	2 Internet of Things History
	3 Internet of Things Architecture
		3.1 Perception Layer
			3.1.1 Perception Node
			3.1.2 Perception Network
		3.2 Network Layer
		3.3 Middleware Layer
			3.3.1 Service Discovery
			3.3.2 Service Composition
			3.3.3 Trustworthiness Management
			3.3.4 Service APIs
		3.4 Application Layer
	4 The Required Technologies
		4.1 RFID
		4.2 WSN
		4.3 RFID Sensor Network
		4.4 NFC
		4.5 Arduino
		4.6 Raspberry
		4.7 ZigBee
		4.8 6LowPAN
	5 Applications
		5.1 Transportation and Logistics
		5.2 Healthcare
		5.3 Smart Environments
			5.3.1 Smart Grid
			5.3.2 Smart Cities
			5.3.3 Smart Home
			5.3.4 Smart Industry
			5.3.5 Smart Building
	6 The Challenges and Problems
		6.1 Security
			6.1.1 Authentication
			6.1.2 Authorization
			6.1.3 Privacy
			6.1.4 Confidentiality
		6.2 Cloud
		6.3 Communication
		6.4 Intelligence
	7 Conclusion
	References
IoMT Applications Perspectives: From Opportunities and Security Challenges to Cyber-Risk Management
	1 Introduction
	2 IoMT Cyber-Security Challenges
		2.1 IoMT Security Requirements
		2.2 Technical Issues
		2.3 Regulatory Issues
	3 Related Works
		3.1 Standards, Guidelines, and Best Practices
		3.2 Review of Existent Solutions
		3.3 Discussions
	4 The Security Risk Management Approach
		4.1 Principle
		4.2 Risk Vectors
		4.3 Risk Ratings
		4.4 Risk Assessment
		4.5 Use Case: Patient Monitoring
		4.6 Discussion
	5 Conclusion and Future Work
	References
Cybersecurity Challenges and Implications for the Adoption of Cloud Computing and IoT: DDoS Attacks as an Example
	1 Introduction to Cloud Computing
	2 Cloud Computing Definitions and Architectures
	3 Cloud Characteristics over Deployment and Service Models
	4 DDoS Attacks Evolution Timeline and Future Challenges
	5 IoT Definitions, Common Deployments, and Risks for DDoS
	6 Cloud Computing Defense Mechanisms
	7 Cloud Deployment Models and Associated Attacks Scenarios
		7.1 Cloud Deployment Models
		7.2 DDoS Attack Scenario
	8 Conclusion
	References
Implementation of the C4.5 Algorithm in the Internet of Things Applications
	1 Introduction
	2 Related Works
	3 System Design
	4 Classification Method
	5 Aquarium Monitoring
		5.1 The Sensor Device
		5.2 System Implementation and Evaluation
	6 Ornamental Plant Monitoring
		6.1 The Sensor Device
		6.2 System Implementation and Evaluation
	7 Conclusion and Future Work
	References
Intrusion Detection Systems Using Machine Learning
	1 Introduction
		1.1 Reconnaissance Attacks
		1.2 Access Attacks
		1.3 Denial of Service Attacks
		1.4 KDD CUP 99
		1.5 CICIDS2017
		1.6 Random Forest
		1.7 Support Vector Machine
		1.8 Neural Network Model
		1.9 Gaussian Naïve Bayes
		1.10 Logistic Regression
		1.11 Normal Linear Discriminant Analysis
		1.12 Ensemble-Based Classification
	2 Related Work
	3 Experimental Setup
	4 Analysis
		4.1 Binary Classification
		4.2 Multiclass Classification
			4.2.1 CICIDS2017 Dataset
			4.2.2 KDD CUP 99 Dataset
		4.3 Ensemble-Based Classification
	5 Conclusion
	References
Multivariate Procedure for Modeling and Prediction of Temperature in Punjab, Pakistan
	1 Introduction
	2 Multiple Regression
	3 Multicollinearity
	4 PLS Technique (Selection of Variable)
		4.1 Variable Selection in PLS
		4.2 Distribution-Based Truncation for Variable Selection in PLS
		4.3 SoftPLS
			4.3.1 Uninformative-Variable-Elimination in PLS-(UVE-PLS)
		4.4 Conventional Normality-Based Truncated PLS
		4.5 Violation of Normality Assumption in Truncated PLS
		4.6 Laplace PLS Method
			4.6.1 Computational Structure
			4.6.2 Parameter Tuning
			4.6.3 Cross Validation
			4.6.4 Monte Carlo Cross Simulation
	5 Royston's Test for Multivariate Normality
	6 Results and Discussions
		6.1 Analysis of Punjab Stations Under Different Scenarios
			6.1.1 Islamabad
			6.1.2 Analysis
			6.1.3 Faisalabad District
			6.1.4 Lahore
	7 Conclusion
	References
A New Proposed Model for the Influence of Climate Change on the Tension Anticipation in Hospital Emergencies
	1 Introduction
	2 Related Work
	3 Methods Based on Exponential Smoothing
	4 Linear Regression Methods
	5 The Simple Linear Regression Model
	6 The Multiple Linear Regression Model
	7 The Proposed Approach
		7.1 Step 1. Creating the DB-EMRG Database
		7.2 Step 2. Creating the DB-CL Database
		7.3 Step 3. Creating the DB-RES Database
		7.4 Step 4. Creating a Statistical Model for Anticipating the Tension in Emergencies
	8 Simulation and Result
	9 Conclusion
	References
Statistical Downscaling Modeling for Temperature Prediction
	1 Introduction
	2 Methodology
		2.1 Data Acquisition
		2.2 Feature Selection
			2.2.1 Statistical Downscaling Model Design (SDMD)
			2.2.2 Downscaling Model Application
			2.2.3 Screening of Variables from NCEP/NCAR Variables
		2.3 Choice of Predictors Using SDSM
		2.4 PLS Technique (Selection of Variable)
		2.5 Calibration of the Model Using SDSM
		2.6 Validation of the Model
		2.7 Evaluation of the Efficient Model with Root Mean Square Error
		2.8 Coefficient of Determination of Model (R2)
	3 Results and Discussions
	4 Conclusion
	5 Future Endeavors
	References
UAV-Based IoT Applications for Action Recognition
	1 Introduction
	2 Action Recognition Algorithms
		2.1 Action Recognition Approaches Categorization
		2.2 UAV-Based Action Recognition Approaches
		2.3 Discussion
	3 Dataset
		3.1 VIRAT Dataset (2011)
		3.2 Mini-Drone Dataset (2015)
		3.3 UCLA Aerial Dataset (2015)
		3.4 Okutama-Action Dataset (2017)
		3.5 UAV-GESTURE Dataset (2018)
		3.6 Drone Action Dataset (2019)
		3.7 A Multi-view-point Outdoor Dataset (2020)
		3.8 NEC-DRONE Dataset (2020)
		3.9 ERA Dataset (2020)
		3.10 Drone SAR Dataset (2020)
		3.11 TinyVirat (2020)
		3.12 UAV-Human (2021)
		3.13 Discussion
	4 Conclusions
	References
Federated Learning for Market Surveillance
	1 Introduction
	2 Literature Review
	3 Background
		3.1 Federated Learning
		3.2 Federated Averaging Algorithm
		3.3 Artificial Neural Network
		3.4 RNN
		3.5 Long Short-Term Memory (LSTM)
		3.6 Federated Learning Frameworks
	4 Methodology
		4.1 Dataset
		4.2 Preprocessing
		4.3 LSTM Autoencoder
		4.4 Model Evaluation
			4.4.1 Confusion Matrix
			4.4.2 Recall
			4.4.3 Accuracy
			4.4.4 AUC Score
	5 Results and Discussion
	6 Conclusion
	7 Future Work
	References
Fake News in Social Media: Fake News Themes and Intentional Deception in the News and on Social Media
	1 Introduction
	2 Fake News Explanation and Statistics
	3 Previous Works and Related Concepts
		3.1 Fake News
		3.2 False News
		3.3 Yellow Journalism
		3.4 Pseudo News
		3.5 Hoax News
		3.6 Propaganda News
		3.7 Clickbait
		3.8 Propaganda
		3.9 Satire/Parody
		3.10 Sloppy Journalism
		3.11 Misleading Headlines
		3.12 Biased/Slanted News
		3.13 Fake News Characterisation and Comparison
	4 How to Spot False Information
		4.1 Who Is Sharing the Story
		4.2 Take a Closer Look
		4.3 Check the Facts
		4.4 Check Your Biases
		4.5 Is It a Joke
		4.6 Fact-Checking
	5 Discussion and Conclusion
	6 Conclusion
	References