Luster Regained in Bits and Bytes: A Novel Cyber Incident Risk Prediction Model for Middle and High Schoolers Using Machine Learning

Overview

Physical isolation during the Covid-19 prompted a 45% increase in digital use leading to an increase in cyber incidents. This project seeks to understand the risk impact of prolonged internet use and evaluate opportunities for cyber education to lower such risk. In preparation for subsequent work, the project will learn patterns in distress, and the recovery of affected individuals. A 20-question English-language survey (n= 1,869) was administered to 6th through 12th graders across 4 countries. Analysis of the survey indicated that the number of hours of internet use was found to be a driver of the risk of cyber incidents. In addition to statistical analysis, the methodology used VertexAI’s AutoML to generate an ensemble model to predict risk from usage patterns (length of usage, gaming use etc.). The cyber risk predictor model set has high overall accuracy (f1 score of 0.88) and precision and recall of 0.878. This low-cost approach to personalized risk scores could support periodic evaluation and trending of educational effectiveness in cyber safety. Separately, participants reported a strong association (Spearman rho = 0.957) between distress from cyber incidents and lead time to their recovery. Among the respondents with high distress experiences, there is an urgent need to design support programs to cope with their distress. SOUTH CAROLINA