This book introduces and presents the newest up-to-date methods, approaches and technologies on how to detect child cyberbullying on social media as well as monitor kids E-learning, monitor games designed and social media activities for kids. On a daily basis, children are exposed to harmful content online. There have been many attempts to resolve this issue by conducting methods based on rating and ranking as well as reviewing comments to show the relevancy of these videos to children; unfortunately, there still remains a lack of supervision on videos dedicated to kids. This book also introduces a new algorithm for content analysis against harmful information for kids. Furthermore, it establishes the goal to track useful information of kids and institutes detection of kid’s textual aggression through methods of machine and deep learning and natural language processing for a safer space for children on social media and online and to combat problems, such as lack of supervision, cyberbullying, kid’s exposure to harmful content. This book is beneficial to postgraduate students and researchers' concerns on recent methods and approaches to kids' cybersecurity.
Author(s): Wael Yafooz, Hussain Al-Aqrabi, Arafat Al-Dhaqm, Abdelhamid Emara
Series: Studies in Computational Intelligence, 1080
Publisher: Springer
Year: 2023
Language: English
Pages: 278
City: Cham
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
