Kids Cybersecurity Using Computational Intelligence Techniques

Contents
State-of-the-Art
Everyday Cyber Safety for Students
	1 Introduction
	2 Cyber Security Terms that Everyone Who Uses a Computer Should Know
	3 Identifying Home Threats
	4 Accounts, Data, and Devices
	5 Getting Rid of Zombie Applications and Files
	6 Hijacked Apps
	7 Exorcise Zombie Programs and Apps!
	8 Gaming Can Make You a Target
	9 A Place for Files and All Files in Their Place
	10 Work Locally
	11 Use Proper File-Naming Conventions
	12 Save Often
	13 Create Versions
	14 Backup Your Work
	15 Identifying Data Stored About Your
	16 Email Communications
	17 Web Measurement Tools and Web Surveys
	18 Cookies
	19 Figuring Out Fake Versus Half-Baked News
	20 Protect and Detect
		20.1 Two Factor and Multifactor Authentication (MFA)
		20.2 If You Don't Know Your Router's Userid and Password, Then I Do!
	21 Tips, Tricks, and Techniques to Protect Devices
		21.1 Keep Your Firewall Turned On
		21.2 Install or Update Your Antivirus Software
		21.3 Install or Update Your Antispyware Technology
		21.4 Keep Your Operating System up to Date
		21.5 Be Careful About What You Download
		21.6 Turn Off Your Computer
	22 Respond and Recover
	23 Conclusion
	References
Machine Learning Approaches for Kids’ E-learning Monitoring
	1 Introduction
	2 Related Works
	3 Methodology
		3.1 The Aim of Machine Learning Approaches in Exam Management System
		3.2 The Advantages of Using ML Methods in Identifying Children with Low Performance
		3.3 Issues and Challenges Related to Using ML in Examination
		3.4 Threats Issues Related to Using ML in the Examination
	4 Results and Discussion
	5 Conclusion
	References
Factors Influencing on Online Education Outcomes–An Empirical Study Based on Kids’ Parents
	1 Introduction
	2 Literature Review
	3 Data and Methodology
		3.1 Data
		3.2 Methodology
	4 Research Results
		4.1 Scale Analysis
		4.2 Explotory Factor Analysis
		4.3 Correlation Matrix
		4.4 Estimation Results
	5 Conclusions
	References
Review on the Social Media Management Techniques Against Kids Harmful Information
	1 Introduction
	2 Concept of Harmful Information
	3 Machine Learning
		3.1 Supervised Machine Learning Algorithms
		3.2 Unsupervised Machine Learning
		3.3 Semi-supervised Machine Learning
		3.4 Reinforcement Machine Learning
	4 Deep Learning
		4.1 Long-Short Term Memory (LSTM)
		4.2 Feedforward Neural Network (FNN)
		4.3 Convolutional Neural Network (CNN)
		4.4 Recurrent Neural Network (RNN)
	5 Content Analysis Using Machine Learning
	6 Content Analysis Using Deep Learning
	7 Summary of Revised Papers
		7.1 Content Analysis via Machine Learning
		7.2 Content Analysis via Deep Learning
	8 Challenges in Detecting Harmful Information
	9 Conclusion and Future Work
	References
Review of Information Security Management Frameworks
	1 Introduction
		1.1 Risk Review
		1.2 Risk Management
		1.3 Key Roles of Risk Management
		1.4 Characteristics of Information Security
		1.5 Information Security Frameworks (ISO 27000 Series)
	2 Methodology
	3 Discussion
	4 Conclusion
	References
Database Forensics Field and Children Crimes
	1 Introduction
	2 Methodology
	3 Results and Discussion
	4 Conclusion
	References
From Exhibitionism to Addiction, or Cyber Threats Among Children and Adolescents
	1 Introduction
	2 Cyber Threats
	3 Cyber Security as a Challenge
	4 Internet Addiction
	5 Digital Exhibitionism
	6 Survey Results
	7 Summary
	References
Cyberbullying and Kids Cyber Security
Protection of Users Kids on Twitter Platform Using Naïve Bayes
	1 Introduction
	2 Literature Review
	3 Methodology
		3.1 URL Based and Content Based Spam Detection
		3.2 Preprocessing Technique
		3.3 Feature Extraction
		3.4 Naive Bayes
	4 Experimental Results
	5 Discussion
		5.1 Confusion Matrix Naïve Bayes Model
	6 Conclusion
	7 Future Work
	References
The Impact of Fake News Spread on Social Media on the Children in Indonesia During Covid-19
	1 Introduction
	2 Research Methods
	3 Results and Discussion
		3.1 Evidence from the Spread of Fake News (Hoax and Disinformation) Cases in Indonesia
		3.2 Media Literacy as an Effort to Mitigate Infomedicine Against Fake News in Indonesia
		3.3 Policies/Regulations for Countering Fake News (Fake News) Based on Indonesia’s Law
	4 Conclusion
	References
A Preventive Approach to Weapons Detection for Children Using Quantum Deep Learning
	1 Introduction
	2 Literature Review
	3 Dataset
	4 Methodology
		4.1 Artificial Intelligence
		4.2 Quantum Artificial Intelligence
		4.3 Weapon Detector Using DL and QDL
	5 Results
		5.1 Accuracy
		5.2 Confusion Matrix
		5.3 ROC Curve
		5.4 Precision, Recall, and F1-Score
	6 Conclusion and Future Work
	References
Learning Arabic for Kids Online Using Google Classroom
	1 Introduction
	2 Research Method
	3 Results and Discussion
	4 Conclusions
	References
Child Emotion Recognition via Custom Lightweight CNN Architecture
	1 Introduction
	2 Literature
		2.1 Available Datasets
	3 Proposed Framework
		3.1 Data Scaling and CNN Training
		3.2 Deployment Infrastructure
		3.3 Addressing Security
	4 Conclusion
	References
Cybercrime Sentimental Analysis for Child Youtube Video Dataset Using Hybrid Support Vector Machine with Ant Colony Optimization Algorithm
	1 Introduction
		1.1 Cyber Crime
	2 Literature Review
	3 System Design
		3.1 Sentiment Classification Techniques
		3.2 Machine Learning Approach
		3.3 Maximum Entropy
		3.4 Architecture for Ensemble Approach
		3.5 Adaboosting with SVM Method
		3.6 Majority Voting
		3.7 Proposed Hybrid Support Vector Machine with Ant Colony Optimization
	4 Results and Discussion
	5 Conclusion
	References
Cyberbullying Awareness Through Sentiment Analysis Based on Twitter
	1 Introduction
	2 Problem Statement
	3 Literature Review
	4 Sentiment Analysis
		4.1 Specific Description on Sentiment Analysis
	5 Technique Descriptions on Sentiment Analysis
		5.1 Naïve Bayes Classifier
		5.2 Naïve Bayes Classifier, Support Vector Machines and Convolutional Neural Network
		5.3 Lexicon Based Approaches, Fuzzy Systems, Supervised Learning, and Statistical Approaches
		5.4 Support Vector Machine
	6 Common Features Related to Twitter
	7 Conclusion
	References
The Impact of Fake News on Kid’s Life from the Holy Al-Qur’an Perspective
	1 Introduction
	2 Research Method
	3 Results and Discussion
		3.1 The Impact of Spreading Fake News
		3.2 Efforts to Prevent the Spread of Fake News
	4 Conclusions
	References
Early Prediction of Dyslexia Risk Factors in Kids Through Machine Learning Techniques
	1 Introduction
	2 Related Works
	3 Proposed Methodology for Dyslexia Detection Using Machine Learning Techniques
		3.1 Dataset
		3.2 Data Preprocessing
		3.3 Feature Selection
		3.4 Building and Training Machine Learning Models
		3.5 Experiments
		3.6 Evaluation Metrics
	4 Results and Discussion
	5 Conclusion
	References
Development of Metamodel for Information Security Risk Management
	1 Introduction
	2 Related Works
	3 Methodology and Development Process
	4 Results and Discussion
	5 Conclusion
	References
Detecting Kids Cyberbullying Using Transfer Learning Approach: Transformer Fine-Tuning Models
	1 Introduction
	2 Related Studies
	3 Materials and Methods
		3.1 Dataset Preparation Phase
		3.2 Data-Pre-processing Phase
		3.3 Pertained Models
		3.4 Evaluation Phase
	4 Experiments and Results Discussion
	5 Conclusion
	References
YouTube Sentiment Analysis: Performance Model Evaluation
	1 Introduction
	2 Related Works
	3 Overview of the Proposed Model
		3.1 Dataset Description
		3.2 Data Pre-processing
		3.3 Annotations
		3.4 Feature Extraction
		3.5 Machine Learning Classifiers
		3.6 Model Evaluation
	4 Results and Discussion
	5 Conclusion
	References