Log-File Analysis to Identify Internet-addiction in Children

The problem of the Internet addiction (IA) arose after the rise of the Internet. Some of the Internet users include children and teenagers and they are active in a virtual environment. Most minor users are not well aware of the dangers posed by information abundance. One of these dangers is the IA. Excessive use of the Internet is addictive, and some users experience a high risk of addiction. IA can negatively affect the children's health, psychology, socialization and other activities. There is a great need to the development of forecasting programs and various technological approaches for the identification of IA among Internet users, especially children and adolescents. This article uses machine-learning techniques to detect IA. Activities of children in the Internet environment is analyzed. The log-files of children and their IA problem are explored. To determine the degree of IA among children and adolescents an experiment is conducted on public dataset. The effectiveness of the methods is analyzed by various evaluation metrics and promising results are obtained.The results show better performance of Weighted SVM, compared to BernoulliNB, Logistic Regression, MLPClassifier, SVM classifiers. Acquired results of the research provide kids information security. To evaluate a kids IA helps to identify their psychological conditions, and it creates a better situation for parents, teachers, and other related people to communicate with children and teenagers better way.