Behavioural Analysis of Susceptible Internet Users

Machine Learning

The Internet has become an integral part of our everyday life. Unfortunately, not all of us are equally aware of the threats when we use online services. Naive users are generally less aware of security and privacy practices on the Internet and are susceptible to online predators. In this paper, we present a behavioral analysis of Internet users and their susceptibility online malpractices. We have considered the dataset from the Global Internet User Survey for 10789 respondents to perform a security-oriented statistical analysis of correlated user behavior. We constructed logistic regression models to analyze the statistical predictability of susceptible and not-so-susceptible identity theft victims based on their behavior and knowledge of security and privacy practices. We posit that such a study can be used to assess the vulnerability of Internet users and can hence be used to leverage institutional and personal safety on the Internet by promoting online security education, threat awareness, and guided Internet-safe behavior.

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