Kids Cybersecurity Using Computational Intelligence Techniques: (1080 Studies in Computational Intelligence)

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.

Table of Contents:
Part 1: State-of-the-art.- Everyday Cyber Safety for Students.- Machine Learning Approaches for Kids’ E-learning Monitoring.- Factors influencing on online education outcomes– an empirical study based on Kids’ parents.- Review on the Social Media Management Techniques against kids Harmful Information.- Review of Information Security Management Frameworks.- Database Forensics Field and Children Crimes.- From exhibitionism to addiction, or cyber threats among children and adolescents.- Part II: Cyberbullying and Kids cyber security.- Protection of Users Kids on Twitter Platform using Naïve Bayes.- The Impact of Fake News Spread on Social Media on The Children in Indonesia During Covid-19.- A Preventive Approach to Weapons Detection for Children Using Quantum Deep Learning.- Learning Arabic for Kids online Using Google Classroom.- Child Emotion Recognition Via Custom Lightweight CNN Architecture.- Cybercrime Sentimental Analysis for Child YouTube Video Dataset Using Hybrid Support VectorMachine With Ant Colony Optimization Algorithm.- Cyberbullying Awareness Through Sentiment Analysis Based On Twitter.- The Impact of Fake News on Kid’s Life from the Holy Al-Qur'an Perspective.- Early Prediction of Dyslexia Risk Factors in Kids through Machine Learning Techniques.- Development of Metamodel for Information Security Risk Management.- Detecting Kids Cyberbullying Using Transfer Learning Approach from Transformer Fine-Tuning Models.- YouTube Sentiment Analysis: Performance Model Evaluation.

About the Author :
Wael M.S. Yafooz is an associate professor in the Computer Science Department, Taibah University, Saudi Arabia. He was an associate professor in the information technology department at Al-Madinah international university(MEDIU)-Malaysia. He was a dean of the faculty of computer and information technology. He received his bachelor degree in area of computer science from Egypt in 2002 while a Master of Science in Computer Science from the University of MARA Technology (UiTM) 2010 as well as a PhD in Computer Science in 2014 from UiTM. He awarded many Gold and Silver Medals for his contribution to a local and international expo of innovation and invention in the area of computer science. Besides, he awarded the Excellent Research Award from UiTM. He served as a member of various committees in many international conferences. Additionally, he chaired IEEE international conference on smart computing and electronic enterprise 2018. Moreover, he supervised many students at themaster and PhD level. Furthermore, He delivered and conducted many workshops in the research area and practical courses in data management and visualization. He invited as a speaker in many international conferences held in Bangladesh, Thailand, India, China, Japan and Russia. His research interest includes Big Data, Data Mining, Machine Learning and Data Management. Dr Hussain Al-Aqrabi is a Senior Lecturer (A. Professor) in Cyber Security at the University of Huddersfield, UK. He received the M.Sc. degree in Computer Networks and the PhD in Cloud Security from the University of Derby, U.K. Hussain is a Fellow of the Higher Education Academy (FHEA). In addition to his University education, He holds EC-Council Certified Ethical Hacker (CEH), Microsoft Certified IT Professional certification (MCITP) on Windows Server, and he is also Cisco Certified in Routing and Switching. Dr Al-Aqrabi has published more than 40 papers in peer-reviewed journals, international conferences, and book series. Dr Al-Aqrabi is a reviewer of high-impact-factor journals such as the IEEE Internet of Things Journal, IEEE Transactions on Cloud Computing, IEEE Transactions on Network and Science Engineering, IEEE Transactions on Cognitive Communications Networking, and the International Journal of Distributed Sensor Networks.   He researches interest Cloud Security, Cyber Secuirty, Multiparty authentication, Network security and optimisation, Secure protocol development and evaluation, The Industrial Internet of Things, Artificial Intelligence. Abdel-Hamid Emara is an associate professor in the Department of Computers and Systems Engineering, Faculty of Engineering, Al-Azhar University, Cairo, Egypt He received his BSc, MSc, and PhD in computers and Systems engineering from Al-Azhar university in 1992, 2000, 2006, respectively. He supervised number of students at the master and PhD levels. Furthermore, He delivered and conducted many workshops in the research area He served as a member of various committees in many international conferences. He is a volunteer reviewer with different peer-review journals. He is currently an Assistant Professor in Computer Science Department at University of Taibah at Al- Madinah Al Monawarah, KSA. His research interest includes, Data Mining, Machine Learning, Deep Learning, Natural Language Processing, Social Network Analytics and Data Management. He published several research papers and participated in several in International journal and local/international conferences. ARAFAT AL-DHAQM is currently working as a senior lecturer at University Technology Malaysia (UTM). Dr. Arafat received his Ph.D in Computer Science from the University Technology Malaysia (UTM). His doctoral research focused on solving the heterogeneity and ambiguity of the database forensic investigation field using a meta-modeling approach. Dr. Arafat has also completed a MSc (Hons.). in Information Security from the University Technology Malaysia (UTM), and a BSc. in Information System from University Technology of Iraq. Dr. Arafat’s current research interests span the domains of digital forensics and cybersecurity.