Kids Cybersecurity Using Computational Intelligence Techniques

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. Contents 6 State-of-the-Art 8 Everyday Cyber Safety for Students 9 1 Introduction 9 2 Cyber Security Terms that Everyone Who Uses a Computer Should Know 10 3 Identifying Home Threats 13 4 Accounts, Data, and Devices 15 5 Getting Rid of Zombie Applications and Files 16 6 Hijacked Apps 17 7 Exorcise Zombie Programs and Apps! 17 8 Gaming Can Make You a Target 17 9 A Place for Files and All Files in Their Place 18 10 Work Locally 18 11 Use Proper File-Naming Conventions 19 12 Save Often 19 13 Create Versions 19 14 Backup Your Work 20 15 Identifying Data Stored About Your 20 16 Email Communications 20 17 Web Measurement Tools and Web Surveys 21 18 Cookies 21 19 Figuring Out Fake Versus Half-Baked News 22 20 Protect and Detect 23 20.1 Two Factor and Multifactor Authentication (MFA) 23 20.2 If You Don't Know Your Router's Userid and Password, Then I Do! 24 21 Tips, Tricks, and Techniques to Protect Devices 26 21.1 Keep Your Firewall Turned On 26 21.2 Install or Update Your Antivirus Software 26 21.3 Install or Update Your Antispyware Technology 26 21.4 Keep Your Operating System up to Date 26 21.5 Be Careful About What You Download 27 21.6 Turn Off Your Computer 27 22 Respond and Recover 28 23 Conclusion 29 References 29 Machine Learning Approaches for Kids’ E-learning Monitoring 31 1 Introduction 31 2 Related Works 32 3 Methodology 34 3.1 The Aim of Machine Learning Approaches in Exam Management System 34 3.2 The Advantages of Using ML Methods in Identifying Children with Low Performance 36 3.3 Issues and Challenges Related to Using ML in Examination 38 3.4 Threats Issues Related to Using ML in the Examination 38 4 Results and Discussion 38 5 Conclusion 39 References 39 Factors Influencing on Online Education Outcomes–An Empirical Study Based on Kids’ Parents 43 1 Introduction 43 2 Literature Review 45 3 Data and Methodology 46 3.1 Data 46 3.2 Methodology 47 4 Research Results 48 4.1 Scale Analysis 48 4.2 Explotory Factor Analysis 48 4.3 Correlation Matrix 50 4.4 Estimation Results 50 5 Conclusions 53 References 54 Review on the Social Media Management Techniques Against Kids Harmful Information 56 1 Introduction 57 2 Concept of Harmful Information 57 3 Machine Learning 58 3.1 Supervised Machine Learning Algorithms 59 3.2 Unsupervised Machine Learning 62 3.3 Semi-supervised Machine Learning 62 3.4 Reinforcement Machine Learning 62 4 Deep Learning 63 4.1 Long-Short Term Memory (LSTM) 63 4.2 Feedforward Neural Network (FNN) 63 4.3 Convolutional Neural Network (CNN) 64 4.4 Recurrent Neural Network (RNN) 65 5 Content Analysis Using Machine Learning 65 6 Content Analysis Using Deep Learning 66 7 Summary of Revised Papers 66 7.1 Content Analysis via Machine Learning 67 7.2 Content Analysis via Deep Learning 67 8 Challenges in Detecting Harmful Information 67 9 Conclusion and Future Work 69 References 70 Review of Information Security Management Frameworks 73 1 Introduction 73 1.1 Risk Review 73 1.2 Risk Management 74 1.3 Key Roles of Risk Management 75 1.4 Characteristics of Information Security 75 1.5 Information Security Frameworks (ISO 27000 Series) 75 2 Methodology 80 3 Discussion 81 4 Conclusion 83 References 84 Database Forensics Field and Children Crimes 85 1 Introduction 85 2 Methodology 88 3 Results and Discussion 92 4 Conclusion 94 References 94 From Exhibitionism to Addiction, or Cyber Threats Among Children and Adolescents 97 1 Introduction 97 2 Cyber Threats 99 3 Cyber Security as a Challenge 100 4 Internet Addiction 102 5 Digital Exhibitionism 103 6 Survey Results 105 7 Summary 108 References 109 Cyberbullying and Kids Cyber Security 8 Protection of Users Kids on Twitter Platform Using Naïve Bayes 112 1 Introduction 113 2 Literature Review 114 3 Methodology 116 3.1 URL Based and Content Based Spam Detection 117 3.2 Preprocessing Technique 117 3.3 Feature Extraction 117 3.4 Naive Bayes 118 4 Experimental Results 118 5 Discussion 120 5.1 Confusion Matrix Naïve Bayes Model 120 6 Conclusion 122 7 Future Work 122 References 123 The Impact of Fake News Spread on Social Media on the Children in Indonesia During Covid-19 124 1 Introduction 125 2 Research Methods 127 3 Results and Discussion 128 3.1 Evidence from the Spread of Fake News (Hoax and Disinformation) Cases in Indonesia 128 3.2 Media Literacy as an Effort to Mitigate Infomedicine Against Fake News in Indonesia 136 3.3 Policies/Regulations for Countering Fake News (Fake News) Based on Indonesia’s Law 138 4 Conclusion 139 References 140 A Preventive Approach to Weapons Detection for Children Using Quantum Deep Learning 143 1 Introduction 144 2 Literature Review 145 3 Dataset 147 4 Methodology 148 4.1 Artificial Intelligence 148 4.2 Quantum Artificial Intelligence 148 4.3 Weapon Detector Using DL and QDL 149 5 Results 150 5.1 Accuracy 151 5.2 Confusion Matrix 151 5.3 ROC Curve 152 5.4 Precision, Recall, and F1-Score 153 6 Conclusion and Future Work 153 References 155 Learning Arabic for Kids Online Using Google Classroom 157 1 Introduction 158 2 Research Method 160 3 Results and Discussion 160 4 Conclusions 163 References 163 Child Emotion Recognition via Custom Lightweight CNN Architecture 166 1 Introduction 166 2 Literature 167 2.1 Available Datasets 169 3 Proposed Framework 170 3.1 Data Scaling and CNN Training 171 3.2 Deployment Infrastructure 172 3.3 Addressing Security 173 4 Conclusion 173 References 174 Cybercrime Sentimental Analysis for Child Youtube Video Dataset Using Hybrid Support Vector Machine with Ant Colony Optimization Algorithm 176 1 Introduction 177 1.1 Cyber Crime 178 2 Literature Review 179 3 System Design 181 3.1 Sentiment Classification Techniques 182 3.2 Machine Learning Approach 182 3.3 Maximum Entropy 183 3.4 Architecture for Ensemble Approach 183 3.5 Adaboosting with SVM Method 185 3.6 Majority Voting 185 3.7 Proposed Hybrid Support Vector Machine with Ant Colony Optimization 186 4 Results and Discussion 189 5 Conclusion 193 References 193 Cyberbullying Awareness Through Sentiment Analysis Based on Twitter 195 1 Introduction 195 2 Problem Statement 196 3 Literature Review 196 4 Sentiment Analysis 199 4.1 Specific Description on Sentiment Analysis 199 5 Technique Descriptions on Sentiment Analysis 201 5.1 Naïve Bayes Classifier 201 5.2 Naïve Bayes Classifier, Support Vector Machines and Convolutional Neural Network 201 5.3 Lexicon Based Approaches, Fuzzy Systems, Supervised Learning, and Statistical Approaches 202 5.4 Support Vector Machine 204 6 Common Features Related to Twitter 209 7 Conclusion 210 References 211 The Impact of Fake News on Kid’s Life from the Holy Al-Qur’an Perspective 212 1 Introduction 213 2 Research Method 215 3 Results and Discussion 215 3.1 The Impact of Spreading Fake News 216 3.2 Efforts to Prevent the Spread of Fake News 219 4 Conclusions 220 References 221 Early Prediction of Dyslexia Risk Factors in Kids Through Machine Learning Techniques 224 1 Introduction 225 2 Related Works 226 3 Proposed Methodology for Dyslexia Detection Using Machine Learning Techniques 228 3.1 Dataset 229 3.2 Data Preprocessing 231 3.3 Feature Selection 232 3.4 Building and Training Machine Learning Models 233 3.5 Experiments 233 3.6 Evaluation Metrics 234 4 Results and Discussion 235 5 Conclusion 238 References 240 Development of Metamodel for Information Security Risk Management 242 1 Introduction 243 2 Related Works 243 3 Methodology and Development Process 245 4 Results and Discussion 247 5 Conclusion 249 References 251 Detecting Kids Cyberbullying Using Transfer Learning Approach: Transformer Fine-Tuning Models 253 1 Introduction 253 2 Related Studies 255 3 Materials and Methods 257 3.1 Dataset Preparation Phase 257 3.2 Data-Pre-processing Phase 258 3.3 Pertained Models 258 3.4 Evaluation Phase 259 4 Experiments and Results Discussion 260 5 Conclusion 261 References 263 YouTube Sentiment Analysis: Performance Model Evaluation 266 1 Introduction 267 2 Related Works 268 3 Overview of the Proposed Model 269 3.1 Dataset Description 269 3.2 Data Pre-processing 270 3.3 Annotations 271 3.4 Feature Extraction 272 3.5 Machine Learning Classifiers 273 3.6 Model Evaluation 273 4 Results and Discussion 274 5 Conclusion 277 References 277